Distributed Resource Control System and Method Based on Cross-Domain Dynamic Coupling

Abstract

The present invention discloses a distributed resource control system and method based on cross-domain dynamic coupling. The system includes: a spatio-temporal dynamic coupling analysis module, which is used to analyze the collected distributed resource operation data to obtain the spatio-temporal distribution of distributed resources, and construct an adjustable resource scheduling optimization model, and input the actual resource data to be analyzed into the adjustable resource scheduling optimization model to generate a resource optimization scheduling plan; a multi-level differential regulation module, which is used to generate a multi-level differential regulation model according to the resource optimization scheduling plan and the hierarchical and grouped architecture, so as to adopt different regulation strategies for the regional power grid, distribution network and microgrid in the hierarchical and grouped architecture; a cloud-edge collaborative synchronization module, which is used to send the regulation indicators generated according to the regulation strategy to the edge devices, and adjust the multi-level differential regulation model according to the feedback data collected by the edge devices. Implementing the present invention realizes the effective regulation of a large amount of distributed resources and improves the regulation efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of distributed resource control, and more particularly to a distributed resource control system and method based on cross-domain dynamic coupling. Background Art

[0002] The large-scale integration of distributed renewable energy and the shift in fossil energy consumption toward electricity consumption are leading to significant changes on both the supply and demand sides of the power grid. On the supply side, there are challenges such as insufficient access to renewable energy and scaled sensing capabilities, poor power quality, and a lack of localized coordinated control. On the demand side, electricity load is characterized by continued rigid growth in total demand and irregular electricity consumption, leading to challenges such as insufficient capacity to absorb distributed renewable energy and a lack of local autonomy.

[0003] Therefore, how to help the power grid eliminate uncertainties and contradictions on both the supply and demand sides and solve the problems faced by the supply and demand balance has become the focus of long-term research in the industry. At this stage, according to the overall idea of multi-source secure access and layered aggregated control, key technologies such as new energy access, carbon tracking, carbon verification, and on-site balance of source, grid, and load are studied to promote the transformation of the power grid from a traditional "passive" one-way radiation network to an "active" two-way interactive system, and from a single power supply and distribution service entity to an efficient configuration platform for source, grid, load, and storage resources. This will support the construction of a new power system with green, low-carbon, flexible, and digitally intelligent characteristics, and help fully utilize distributed new energy and achieve low-carbon development. In response to the problems faced by the integration of source, grid, load, and storage, the existing control system mainly adopts a centralized control mode, which uses average decomposition and indicators to control the household through the cloud. It cannot effectively control massive distributed resources, and the control strategy is slow to generate, resulting in low control efficiency. Summary of the Invention

[0004] The purpose of the present invention is to provide a distributed resource control system and method based on cross-domain dynamic coupling to solve the problem that the existing control system cannot effectively regulate massive distributed resources and the regulation efficiency is low.

[0005] To achieve the above object, on the one hand, the present invention provides a distributed resource control system based on cross-domain dynamic coupling, including: a spatio-temporal dynamic coupling analysis module, configured to analyze the variation law and distribution characteristics of the collected distributed resource operation data in the spatio-temporal dimension to obtain the spatio-temporal distribution of distributed resources, construct an adjustable resource scheduling optimization model based on the spatio-temporal distribution of distributed resources, and input the actual resource data to be analyzed into the adjustable resource scheduling optimization model to generate a resource optimization scheduling plan; a multi-level differential regulation module, configured to generate a multi-level differential regulation model according to the resource optimization scheduling plan and the hierarchical and grouped architecture, so as to adopt different regulation strategies for the regional power grid, distribution network, and microgrid in the hierarchical and grouped architecture; a cloud-edge collaborative synchronization module, configured to generate regulation indicators according to the regulation strategy, send the regulation indicators to the edge devices, and adjust the multi-level differential regulation model according to the feedback data collected by the edge devices.

[0006] Its further technical solution is: the spatio-temporal dynamic coupling analysis module includes: a collection and preprocessing module, configured to collect distributed resource operation data, and clean and classify the distributed resource operation data to obtain distributed resource classification data; a spatio-temporal analysis module, configured to perform spatio-temporal feature analysis on the distributed resource classification data to obtain the spatio-temporal distribution of distributed resources; a construction module, configured to solve the optimization model by using the particle swarm optimization method based on the spatio-temporal distribution of distributed resources, the designed optimization objective function, and the established multi-dimensional constraint conditions, so as to construct the adjustable resource scheduling optimization model; a model verification and optimization module, configured to verify the adjustable resource scheduling optimization model to obtain a verification result, and adjust the model parameters of the adjustable resource scheduling optimization model according to the verification result to optimize the adjustable resource scheduling optimization model; a scheduling generation module, configured to input the actual resource data to be analyzed into the verified and optimized adjustable resource scheduling optimization model for scheduling analysis to generate the resource optimization scheduling plan.

[0007] Its further technical solution is: the multi-level differential regulation module includes: a regional power grid regulation module, configured to generate an economic and low-carbon operation regulation strategy based on the resource optimization scheduling plan and the deep neural network model; a distribution network regulation module, configured to establish a task index decomposition system based on the resource optimization scheduling plan, and adopt a multi-round response mechanism on a daily and intraday basis to roll-execute the tasks in the task index decomposition system, and monitor the accuracy of the executed tasks to generate an intensive management strategy; a microgrid regulation module, configured to carry out line power flow monitoring, analyze the line operation conditions, and generate an interconnection and partition autonomy strategy according to the resource optimization scheduling plan when the interconnection conditions are met.

[0008] Its further technical solution is as follows: The regional power grid regulation module includes: a condition input determination module, which is used to take the green power consumption situation, energy storage configuration, and typical daily load curve in the regional power grid as regional input data, and take the power generation capacity, power consumption demand, and carbon emission in the regional power grid as regional constraint conditions; a regional strategy generation module, which is used to input the regional input data and the regional constraint conditions into the deep neural network model to output the economic and low-carbon operation regulation strategy, and optimize and adjust the economic and low-carbon operation regulation strategy according to the resource optimal scheduling plan, wherein the economic and low-carbon operation regulation strategy includes annual power generation and consumption indicators, quarterly power generation and consumption indicators, monthly power generation and consumption indicators, annual carbon emission indicators, quarterly carbon emission indicators, and monthly carbon emission indicators.

[0009] Its further technical solution is as follows: The distribution network regulation module includes: a task index establishment module, which is used to establish the task index decomposition system of the day-ahead source-load prediction information and the intra-day source-load prediction information based on the spatio-temporal distribution of the distributed resources in the resource optimal scheduling plan, and put the tasks in the task index decomposition system into the multi-source load resource pool; a task execution monitoring module, which is used to regulate the daily execution tasks by adopting a multi-round response mechanism for the day-ahead and intra-day, and roll-execute the tasks in the multi-source load resource pool, monitor the accuracy of the executed tasks, and when the accuracy is lower than the threshold, start the next round of regulation, so as to generate the intensive management strategy.

[0010] Its further technical solution is as follows: The microgrid regulation module includes: a detection module, which is used to detect line overload to judge whether the mutual assistance condition is met; a microgrid strategy generation module, which is used to calculate the adjustable capacity required for line overload when the mutual assistance condition is met, obtain the priority weighting value by weighting factors such as the influence degree of each substation load on line overload, adjustable load capacity, and load type, sort the adjustable capacity required for overload according to the priority weighting value to obtain the multi-level priority adjustable capacity, and take the adjustable capacity of the i-th priority as the allocation capacity, where i = 1; judge whether the allocation capacity is greater than the remaining adjustment capacity; if the allocation capacity is greater than the remaining adjustment capacity, decompose the remaining adjustment capacity to each substation according to the proportion of the capacity of each substation, and output the priority adjustment instructions corresponding to each substation to generate the mutual assistance and zoning autonomy strategy.

[0011] Its further technical solution is as follows: The microgrid regulation module further includes: an update execution module, which is used to, if the allocation capacity is not greater than the remaining adjustment capacity, let all of the allocation capacity participate in the adjustment, update the remaining adjustment capacity, and make i = i + 1, take the adjustable capacity of the i-th priority as the allocation capacity, and return to execute the step of judging whether the allocation capacity is greater than the remaining adjustment capacity until all of the multi-level priority adjustable capacity participates in the adjustment.

[0012] Its further technical solution is as follows: The cloud-edge collaborative synchronization module includes: a sending module, configured to send a regulation instruction corresponding to the regulation index to the edge device; a regulation evaluation module, configured to evaluate the regulation effect according to the feedback data uploaded by the edge device to generate an effect evaluation result, and adjust the hierarchical differential regulation model according to the effect evaluation result.

[0013] Its further technical solution is as follows: The cloud-edge collaborative synchronization module further includes: a source-load prediction module, configured to perform source-load prediction according to environmental data, weather data, and historical data; a positioning and pushing module, configured to locate fault information and push the fault information in real time.

[0014] To achieve the above object, on the other hand, the present invention also provides a distributed resource control method based on cross-domain dynamic coupling, including: analyzing the variation law and distribution characteristics of the collected distributed resource operation data in the spatio-temporal dimension through a spatio-temporal dynamic coupling analysis module to obtain the spatio-temporal distribution of distributed resources, constructing an adjustable resource scheduling optimization model based on the spatio-temporal distribution of distributed resources, inputting the actual resource data to be analyzed into the adjustable resource scheduling optimization model to generate a resource optimization scheduling plan; generating a hierarchical differential regulation model according to the resource optimization scheduling plan and the hierarchical and grouped architecture through a multi-level differential regulation module to adopt different regulation strategies for the regional power grid, distribution network, and microgrid in the hierarchical and grouped architecture; generating a regulation index according to the regulation strategy through a cloud-edge collaborative synchronization module and sending the regulation index to the edge device.

[0015] The embodiment of the present invention provides a distributed resource control system and method based on cross-domain dynamic coupling. Among them, the system first generates a resource optimization scheduling plan through a spatio-temporal dynamic coupling analysis module based on the constructed adjustable resource scheduling optimization model; then quickly generates different regulation strategies through a multi-level differential regulation module according to the resource optimization scheduling plan and the hierarchical and grouped architecture to regulate the regional power grid, distribution network, and microgrid; finally, sends the regulation index through a cloud-edge collaborative synchronization module and adjusts the multi-level differential regulation model according to the feedback data to make the generated regulation strategy more accurate, thereby realizing the effective regulation of a large amount of distributed resources and improving the regulation efficiency.

[0016] Through the following description and in combination with the accompanying drawings, the present invention will become clearer, and these drawings are used to explain the embodiments of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a block diagram of a distributed resource control system based on cross-domain dynamic coupling of the present invention;

[0018] Figure 2 Schematic diagram of the hierarchical and grouped architecture in a distributed resource control system based on cross - domain dynamic coupling of the present invention;

[0019] Figure 3 Block diagram of the spatio - temporal dynamic coupling analysis module in a distributed resource control system based on cross - domain dynamic coupling of the present invention;

[0020] Figure 4 Block diagram of the multi - level differential regulation module in a distributed resource control system based on cross - domain dynamic coupling of the present invention;

[0021] Figure 5 Block diagram of the regional power grid regulation module in a distributed resource control system based on cross - domain dynamic coupling of the present invention;

[0022] Figure 6 Block diagram of the distribution network regulation module in a distributed resource control system based on cross - domain dynamic coupling of the present invention;

[0023] Figure 7 Block diagram of the micro - grid regulation module in a distributed resource control system based on cross - domain dynamic coupling of the present invention;

[0024] Figure 8 Block diagram of the cloud - edge collaborative synchronization module in a distributed resource control system based on cross - domain dynamic coupling of the present invention;

[0025] Figure 9 Flow schematic diagram of a distributed resource control method based on cross - domain dynamic coupling according to an embodiment of the present invention;

[0026] Reference numerals:

[0027] 10. Distributed resource control system based on cross - domain dynamic coupling; 11. Spatio - temporal dynamic coupling analysis module; 111. Acquisition and pre - processing module; 112. Spatio - temporal analysis module; 113. Construction module; 114. Model verification and optimization module; 115. Scheduling generation module; 12. Multi - level differential regulation module; 121. Regional power grid regulation module; 1211. Condition input determination module; 1212. Regional policy generation module; 122. Distribution network regulation module; 1221. Task index establishment module; 1222. Task execution monitoring module; 123. Micro - grid regulation module; 1231. Detection module; 1232. Micro - grid policy generation module; 1233. Update and execution module; 13. Cloud - edge collaborative synchronization module; 131. Issuing module; 132. Regulation evaluation module; 133. Source - load prediction module; 134. Location and push module. Detailed implementation manners

[0028] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Similar component numbers in the drawings represent similar components. Obviously, the embodiments described below are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0029] Referring to Figures 1 to 8 , the distributed resource control system 10 based on cross-domain dynamic coupling provided by the embodiments of the present invention includes a spatio-temporal dynamic coupling analysis module 11, a multi-level differential regulation module 12, and a cloud-edge collaborative synchronization module 13. Among them, the spatio-temporal dynamic coupling analysis module 11 is used to analyze the change rules and distribution characteristics of the collected distributed resource operation data in the spatio-temporal dimension to obtain the spatio-temporal distribution of distributed resources, construct an adjustable resource scheduling optimization model based on the spatio-temporal distribution of distributed resources, and input the actual resource data to be analyzed into the adjustable resource scheduling optimization model to generate a resource optimization scheduling plan; the multi-level differential regulation module 12 is used to generate a multi-level differential regulation model according to the resource optimization scheduling plan and the hierarchical and clustering architecture, so as to adopt different regulation strategies for the regional power grid, distribution network, and microgrid in the hierarchical and clustering architecture; the cloud-edge collaborative synchronization module 13 is used to generate regulation indicators according to the regulation strategies, send the regulation indicators to the edge devices, and adjust the multi-level differential regulation model according to the feedback data collected by the edge devices. It should be noted that, in this embodiment, the distributed resource control system 10 based on cross-domain dynamic coupling is configured in a computer device in the cloud, and the functions of the distributed resource control system 10 based on cross-domain dynamic coupling are realized by executing corresponding software programs in the computer device. It should also be noted that, in this embodiment, first, the spatio-temporal dynamic coupling analysis module 11 generates the resource optimization scheduling plan based on the constructed adjustable resource scheduling optimization model; then, according to the resource optimization scheduling plan and the hierarchical and clustering architecture, the multi-level differential regulation module 12 quickly generates different regulation strategies to regulate the regional power grid, distribution network, and microgrid; finally, the cloud-edge collaborative synchronization module 13 sends the regulation indicators and adjusts the multi-level differential regulation model according to the feedback data, so that the generated regulation strategies are more accurate, thereby realizing the effective regulation of a large number of distributed resources and improving the regulation efficiency.

[0030] In some embodiments, such as this embodiment, as Figure 2As shown, the hierarchical and clustering architecture realizes the responsibility sharing of the regional power distribution and utilization network by dividing the power balance areas at different levels. Specifically, the regional power distribution and utilization network includes a regional power grid, a distribution network, and a microgrid. Among them, the regional power grid is composed of a cluster of interconnected distribution networks, the distribution network is composed of a cluster of interconnected microgrids, and each microgrid has the capabilities of power generation, load, intelligent control, and communication, and can achieve local power generation / load scheduling and feedback control to maintain the net power balance and optimize operation. In the event of a fault, the microgrid can also alleviate the fault problem by reducing power generation or load.

[0031] In some embodiments, such as in this embodiment, as Figure 3As shown in the figure, the spatio-temporal dynamic coupling analysis module 11 includes a collection and preprocessing module 111, a spatio-temporal analysis module 112, a construction module 113, a model verification and optimization module 114, and a scheduling generation module 115. Among them, the collection and preprocessing module 111 is used to collect the operation data of distributed resources, and clean and classify the operation data of distributed resources to obtain classified data of distributed resources; the spatio-temporal analysis module 112 is used to perform spatio-temporal feature analysis on the classified data of distributed resources to obtain the spatio-temporal distribution of distributed resources; the construction module 113 is used to solve the optimization model by using the particle swarm optimization method based on the spatio-temporal distribution of distributed resources, the designed optimization objective function, and the established multi-dimensional constraint conditions, so as to construct the adjustable resource scheduling optimization model; the model verification and optimization module 114 is used to verify the adjustable resource scheduling optimization model to obtain the verification result, and adjust the model parameters of the adjustable resource scheduling optimization model according to the verification result to optimize the adjustable resource scheduling optimization model; the scheduling generation module 115 is used to input the actual resource data to be analyzed into the verified and optimized adjustable resource scheduling optimization model for scheduling analysis to generate the resource optimization scheduling plan. It should be noted that in this embodiment, the operation data of distributed resources includes data such as photovoltaic output data, load data, geographical location, and timestamp of distributed resources; cleaning the operation data of distributed resources is to eliminate outliers and ensure the accuracy of the data; according to urban-rural differences, such as load density, resource type, grid structure, etc., the cleaned operation data of distributed resources is classified to obtain the classified data of distributed resources. It should also be noted that in this embodiment, performing spatio-temporal feature analysis on the classified data of distributed resources to obtain the spatio-temporal distribution of distributed resources specifically means that in the time dimension, analyze the variation rules of the classified data of distributed resources at the hourly and minute levels, such as the intra-day fluctuations of photovoltaic output and the peak-valley characteristics of load, and study the variation trends of the classified data of distributed resources at the seasonal and annual scales, such as seasonal load changes and inter-annual fluctuations of photovoltaic output; in the space dimension, based on the geographic information system, draw the spatial distribution map of distributed resources, which is used to analyze the density and concentration of distributed resources in urban and rural areas, compare the differences in resource type, capacity, adjustable potential, etc. between urban and rural areas, and identify key influencing factors. Further, in this embodiment, the designed optimization objective function is a function designed based on resource utilization rate, supply-demand matching degree, etc., and the multi-dimensional constraint conditions include grid physical constraint conditions, operation constraint conditions, and resource characteristic constraint conditions. The grid physical constraint conditions include line capacity and voltage limit; the operation constraint conditions include power balance and regulation response time, and the resource characteristic constraint conditions include energy storage charge and discharge rate and photovoltaic output fluctuation.Furthermore, in this embodiment, the adjustable resource scheduling optimization model is verified to evaluate its performance under different conditions, and the model parameters of the adjustable resource scheduling optimization model are adjusted to optimize the adjustable resource scheduling optimization model, and further optimize the generated resource optimization scheduling scheme.

[0032] In some embodiments, such as this embodiment, as Figure 4 shown, the multi-level differential regulation module 12 includes a regional power grid regulation module 121, a distribution network regulation module 122, and a microgrid regulation module 123. Among them, the regional power grid regulation module 121 is used to generate an economic and low-carbon operation regulation strategy based on the resource optimization scheduling scheme and the deep neural network model; the distribution network regulation module 122 is used to establish a task index decomposition system based on the resource optimization scheduling scheme, and adopt a multi-round response mechanism for day-ahead and intra-day to roll-execute the tasks in the task index decomposition system, and monitor the accuracy of the executed tasks to generate an intensive management strategy; the microgrid regulation module 123 is used to carry out line power flow monitoring, analyze the line operation conditions, and when the mutual assistance conditions are met, generate a mutual assistance and partition autonomy strategy according to the resource optimization scheduling scheme. It should be noted that in this embodiment, the economic and low-carbon operation regulation strategy aims at economic and low-carbon development, fully considers the regional power generation, power consumption balance, and carbon emission information, and gradually issues annual, quarterly, and monthly execution indicators; the intensive management strategy constructs a multi-source load resource pool to support the efficient resource scheduling of distributed photovoltaic stations, charging stations, energy storage stations, etc. in the distribution network; the mutual assistance and partition autonomy strategy aims at safe operation, suppression of power generation and consumption load fluctuations, and optimization of power quality, realizes cross-domain energy mutual assistance and autonomous operation of the microgrid, and supports microgrid fault alarm recovery and power balance.

[0033] In some embodiments, such as this embodiment, as Figure 5As shown, the regional power grid regulation module 121 includes a condition input determination module 1211 and a regional policy generation module 1212. Among them, the condition input determination module 1211 is used to take the green power consumption situation, energy storage configuration, and typical daily load curve in the regional power grid as regional input data, and take the power generation capacity, power consumption demand, and carbon emissions in the regional power grid as regional constraint conditions; the regional policy generation module 1212 is used to input the regional input data and the regional constraint conditions into the deep neural network model to output the economic and low-carbon operation regulation strategy, and optimize and adjust the economic and low-carbon operation regulation strategy according to the resource optimal scheduling plan. Among them, the economic and low-carbon operation regulation strategy includes annual power generation and consumption indicators, quarterly power generation and consumption indicators, monthly power generation and consumption indicators, annual carbon emissions indicators, quarterly carbon emissions indicators, and monthly carbon emissions indicators. It should be noted that in this embodiment, the generated economic and low-carbon operation regulation strategy aims at medium- and long-term economic (for example, minimizing the operation cost of the regional power grid), low-carbon (for example, maximizing the green power consumption ratio and minimizing carbon emissions), and reliable (for example, ensuring grid stability and improving power supply reliability) operation, and comprehensively considers the energy consumption situation of production and life in the regional power grid. It should also be noted that in this embodiment, medium- and long-term indicators, such as annual power generation and consumption indicators, are decomposed to each level of the power grid, and the indicators are regularly adjusted and optimized according to the actual operation situation and external environment changes to ensure the efficient operation of each level of the power grid under the premise of meeting economic and low-carbon constraints.

[0034] In some embodiments, such as this embodiment, as Figure 6 shown, the distribution network regulation module 122 includes a task index establishment module 1221 and a task execution monitoring module 1222. Among them, the task index establishment module 1221 is used to establish the task index decomposition system of the day-ahead source-load prediction information and the intra-day source-load prediction information based on the spatio-temporal distribution of distributed resources in the resource optimal scheduling plan, and put the tasks in the task index decomposition system into the multi-source load resource pool; the task execution monitoring module 1222 is used to regulate the daily execution tasks by adopting a multi-round response mechanism for the day-ahead and intra-day, and roll-execute the tasks in the multi-source load resource pool, monitor the accuracy of the executed tasks, and start the next round of regulation when the accuracy is lower than the threshold, so as to generate the intensive management strategy. It should be noted that in this embodiment, considering application scenarios such as peak shaving, frequency modulation, voltage regulation, and demand-side response, a multi-source load resource pool is constructed with the microgrid as the intelligent agent, realizing the aggregation-layer cross-domain regulation at the medium- and short-time scales. It should also be noted that in this embodiment, when the accuracy is not lower than the threshold, the regulation continues.

[0035] In some embodiments, such as this embodiment, as Figure 7As shown, the microgrid control module 123 includes a detection module 1231, a microgrid strategy generation module 1232, and an update execution module 1233. Among them, the detection module 1231 is used to detect line overload to determine whether the mutual assistance condition is met; the microgrid strategy generation module 1232 is used to calculate the adjustable capacity required for line overload when the mutual assistance condition is met, obtain the priority weighting value by factor weighting according to the influence degree of each substation area load on line overload, the adjustable load capacity, and the load type, sort the adjustable capacity required for overload according to the priority weighting value to obtain the multi-level priority adjustable capacity, and use the adjustable capacity of the i-th priority as the allocated capacity, where i = 1; judge whether the allocated capacity is greater than the remaining adjustment capacity; if the allocated capacity is greater than the remaining adjustment capacity, decompose the remaining adjustment capacity to each substation area proportionally according to the capacity of each substation area, and output the priority adjustment instructions corresponding to each substation area to generate the mutual assistance and subarea autonomous strategy; the update execution module 1233 is used to if the allocated capacity is not greater than the remaining adjustment capacity, then let all of the allocated capacity participate in the adjustment, update the remaining adjustment capacity, and make i = i + 1, use the adjustable capacity of the i-th priority as the allocated capacity, and return to execute the step of judging whether the allocated capacity is greater than the remaining adjustment capacity until all of the multi-level priority adjustable capacity has participated in the adjustment. It should be noted that in this embodiment, in view of the problems such as the tight spatio-temporal coupling of distributed resources in the distribution network and the difficulty of mutual assistance across substation areas, a mutual assistance and subarea autonomous strategy is established with a preset kilovolt line as the management unit to carry out line power fluctuation suppression and power reverse transmission control. It should also be noted that in other embodiments, the detection module 1231 can also detect overvoltage, undervoltage, three-phase imbalance, etc., and then judge whether the mutual assistance condition is met to ensure the safe and stable operation of the line.

[0036] In certain embodiments, such as this embodiment, for example Figure 8As shown, the cloud-edge collaborative synchronization module 13 includes a distribution module 131, a regulation evaluation module 132, a source-load prediction module 133, and a positioning and pushing module 134. Among them, the distribution module 131 is used to distribute the regulation instructions corresponding to the regulation indicators to the edge devices; the regulation evaluation module 132 is used to evaluate the regulation effect according to the feedback data uploaded by the edge devices to generate an effect evaluation result, and adjust the hierarchical differential regulation model according to the effect evaluation result; the source-load prediction module 133 is used to perform source-load prediction based on environmental data, weather data, and historical data; the positioning and pushing module 134 is used to locate the fault information and push the fault information in real time. It should be noted that in this embodiment, the cloud-edge collaborative synchronization module 13 can solve problems such as insufficient cloud computing power and low timeliness in the regulation scenario of massive distributed resources. Specifically, first, the regulation indicators that meet the actual constraints are quickly generated according to the regulation strategy in the cloud to ensure the rationality and feasibility of the regulation objectives; second, the edge devices achieve second-level / minute-level load monitoring, decompose the regulation indicators issued by the cloud to each household according to the principles of fairness and reasonableness, generate refined regulation instructions and issue them for execution, improving the timeliness and accuracy of regulation. It should be noted that in this embodiment, through the cloud-edge collaborative communication network, the data synchronization between the cloud and the edge devices is realized to ensure the consistency and timeliness of the data; the regulation indicators generated by the cloud and the regulation strategies generated by the edge are synchronized to ensure the consistency of the regulation objectives; the edge devices sense the operation state of the microgrid in real time, collect feedback data and upload it to the cloud, and the cloud evaluates the regulation effect based on the feedback data, identifies the difference between the regulation strategy and the actual demand, and optimizes the hierarchical differential regulation model according to the effect evaluation result to improve the accuracy and adaptability of the regulation. It should also be noted that in this embodiment, the cloud-edge collaborative synchronization module 13 endows functional modules such as source-load prediction, regulation evaluation, and positioning and pushing, improving the intelligent level of the distributed control system.

[0037] Referring to Figure 9 , Figure 9 shows a schematic flowchart of an embodiment of a distributed resource control method based on cross-domain dynamic coupling of the present invention. The distributed resource control method based on cross-domain dynamic coupling is applied to the above-mentioned distributed resource control system based on cross-domain dynamic coupling. The following further elaborates the specific implementation steps of the distributed resource control system based on cross-domain dynamic coupling of the present invention with this method. As Figure 9 shown, this method includes steps S110-S130:

[0038] S110. Analyze the variation laws and distribution characteristics of the collected distributed resource operation data in the spatio-temporal dimension through the spatio-temporal dynamic coupling analysis module to obtain the spatio-temporal distribution of distributed resources. Based on the spatio-temporal distribution of distributed resources, construct an adjustable resource scheduling optimization model, and input the actual resource data to be analyzed into the adjustable resource scheduling optimization model to generate a resource optimization scheduling plan.

[0039] S120. Generate a multi-level differential regulation model through the multi-level differential regulation module according to the resource optimization scheduling plan and the hierarchical and grouped architecture, so as to adopt different regulation strategies for the regional power grid, distribution network, and microgrid in the hierarchical and grouped architecture.

[0040] S130. Generate regulation indicators through the cloud-edge collaborative synchronization module according to the regulation strategy, and send the regulation indicators to the edge devices.

[0041] In the embodiment of the present invention, the spatio-temporal dynamic coupling analysis module analyzes the variation laws and distribution characteristics of the collected distributed resource operation data in the spatio-temporal dimension to obtain the spatio-temporal distribution of distributed resources, so as to construct an adjustable resource scheduling optimization model. Input the actual resource data to be analyzed into the adjustable resource scheduling optimization model to generate a resource optimization scheduling plan. According to the resource optimization scheduling plan and the hierarchical and grouped architecture, quickly generate different regulation strategies through the multi-level differential regulation module to regulate the regional power grid, distribution network, and microgrid. Send the regulation indicators through the cloud-edge collaborative synchronization module, and adjust the multi-level differential regulation model according to the feedback data to make the generated regulation strategy more accurate, thereby realizing the effective regulation of a large amount of distributed resources and improving the regulation efficiency. Further, the spatio-temporal dynamic coupling analysis module includes an acquisition preprocessing module, a spatio-temporal analysis module, a construction module, a model verification and optimization module, and a scheduling generation module. The multi-level differential regulation module includes a regional power grid regulation module, a distribution network regulation module, and a microgrid regulation module. The regional power grid regulation module includes a condition input determination module and a regional strategy generation module. The distribution network regulation module includes a task index establishment module and a task execution monitoring module. The microgrid regulation module includes a detection module, a microgrid strategy generation module, and an update execution module. The cloud-edge collaborative synchronization module includes a sending module, a regulation evaluation module, a source-load prediction module, and a positioning and pushing module. It should be noted that in this embodiment, the specific implementations of the acquisition preprocessing module, the spatio-temporal analysis module, the construction module, the model verification and optimization module, the scheduling generation module, the regional power grid regulation module, the distribution network regulation module, the microgrid regulation module, the condition input determination module, the regional strategy generation module, the task index establishment module, the task execution monitoring module, the detection module, the microgrid strategy generation module, the update execution module, the sending module, the regulation evaluation module, the source-load prediction module, and the positioning and pushing module are as described above. For the sake of simplicity, they will not be elaborated here.

[0042] The present invention has been described in conjunction with the preferred embodiments, but the present invention is not limited to the disclosed embodiments above, and should cover various modifications and equivalent combinations made according to the essence of the present invention.

Claims

1. A distributed resource control system based on cross-domain dynamic coupling, characterized in that Including: A spatio-temporal dynamic coupling analysis module, which is used to analyze the variation laws and distribution characteristics of the collected distributed resource operation data in the spatio-temporal dimension to obtain the spatio-temporal distribution of distributed resources, construct an adjustable resource scheduling optimization model based on the spatio-temporal distribution of distributed resources, and input the actual resource data to be analyzed into the adjustable resource scheduling optimization model to generate a resource optimization scheduling plan; A multi-level differential regulation module, which is used to generate a multi-level differential regulation model according to the resource optimization scheduling plan and the hierarchical and grouped architecture, so as to adopt different regulation strategies for the regional power grid, distribution network and microgrid in the hierarchical and grouped architecture; A cloud-edge collaborative synchronization module, which is used to generate regulation indicators according to the regulation strategy, send the regulation indicators to the edge devices, and adjust the multi-level differential regulation model according to the feedback data collected by the edge devices; Among them, the multi-level differential regulation module includes: A regional power grid regulation module, which is used to generate an economic and low-carbon operation regulation strategy based on the resource optimization scheduling plan and the deep neural network model; A distribution network regulation module, which is used to establish a task index decomposition system based on the resource optimization scheduling plan, and adopt a multi-round response mechanism for the day-ahead and within-day to roll-execute the tasks in the task index decomposition system, and monitor the accuracy of the executed tasks to generate an intensive management strategy; A microgrid regulation module, which is used to carry out line power flow monitoring and analyze the line operation conditions. When the mutual assistance conditions are met, generate a mutual assistance and partition autonomy strategy according to the resource optimization scheduling plan.

2. The distributed resource control system based on cross-domain dynamic coupling as claimed in claim 1, wherein The spatio-temporal dynamic coupling analysis module includes: A collection and preprocessing module, which is used to collect distributed resource operation data, and clean and classify the distributed resource operation data to obtain distributed resource classification data; A spatio-temporal analysis module, which is used to analyze the spatio-temporal characteristics of the distributed resource classification data to obtain the spatio-temporal distribution of distributed resources; A construction module, which is used to solve the optimization model by using the particle swarm optimization method based on the spatio-temporal distribution of distributed resources, the designed optimization objective function and the established multi-dimensional constraint conditions, so as to construct the adjustable resource scheduling optimization model; A model verification and optimization module, which is used to verify the adjustable resource scheduling optimization model to obtain a verification result, and adjust the model parameters of the adjustable resource scheduling optimization model according to the verification result to optimize the adjustable resource scheduling optimization model; A scheduling generation module, which is used to input the actual resource data to be analyzed into the verified and optimized adjustable resource scheduling optimization model for scheduling analysis to generate the resource optimization scheduling plan.

3. The distributed resource control system based on cross-domain dynamic coupling as claimed in claim 1, wherein The regional power grid regulation module includes: A condition input determination module, which is used to use the green power consumption situation, energy storage configuration and typical daily load curve in the regional power grid as regional input data, and use the power generation capacity, power consumption demand and carbon emission in the regional power grid as regional constraint conditions; The regional strategy generation module is used to input the regional input data and the regional constraint conditions into the deep neural network model to output the economic and low-carbon operation regulation strategy, and optimize and adjust the economic and low-carbon operation regulation strategy according to the resource optimization scheduling plan. The economic and low-carbon operation regulation strategy includes annual power generation and consumption indicators, quarterly power generation and consumption indicators, monthly power generation and consumption indicators, annual carbon emission indicators, quarterly carbon emission indicators, and monthly carbon emission indicators.

4. The distributed resource control system based on cross-domain dynamic coupling according to claim 1, wherein The distribution network regulation module includes: The task index establishment module is used to establish the task index decomposition system of the day-ahead source-load prediction information and the intra-day source-load prediction information based on the spatio-temporal distribution of the distributed resources in the resource optimization scheduling plan, and put the tasks in the task index decomposition system into the multi-source load resource pool. The task execution monitoring module is used to regulate the daily execution tasks by adopting a multi-round response mechanism for the day-ahead and intra-day, and roll-execute the tasks in the multi-source load resource pool, monitor the accuracy of the executed tasks, and start the next round of regulation when the accuracy is lower than the threshold, so as to generate the intensive management strategy.

5. The distributed resource control system based on cross-domain dynamic coupling as claimed in claim 1, wherein The microgrid regulation module includes: The detection module is used to detect line overload to judge whether the mutual assistance condition is met. The microgrid strategy generation module is used to calculate the capacity to be adjusted for line overload when the mutual assistance condition is met, obtain the priority weighting value by factor weighting according to the influence degree of each substation load on line overload, the adjustable load capacity, and the load type, sort the capacity to be adjusted for overload according to the priority weighting value to obtain multi-level priority adjustable capacity, and use the i-th priority adjustable capacity as the allocated capacity, where i = 1; judge whether the allocated capacity is greater than the remaining adjustment capacity; if the allocated capacity is greater than the remaining adjustment capacity, decompose the remaining adjustment capacity to each substation according to the proportion of the capacity of each substation, and output the priority adjustment instructions corresponding to each substation to generate the mutual assistance and sub-region autonomous strategy.

6. The distributed resource control system based on cross-domain dynamic coupling according to claim 5, wherein The microgrid regulation module further includes: The update execution module is used to, if the allocated capacity is not greater than the remaining adjustment capacity, participate all the allocated capacity in the adjustment, update the remaining adjustment capacity, and make i = i + 1, use the i-th priority adjustable capacity as the allocated capacity, and return to execute the step of judging whether the allocated capacity is greater than the remaining adjustment capacity until all the multi-level priority adjustable capacity participates in the adjustment.

7. The distributed resource control system based on cross-domain dynamic coupling according to any one of claims 1-6, characterized in that The cloud-edge collaborative synchronization module includes: The sending module is used to send the regulation instructions corresponding to the regulation indicators to the edge devices. The regulation evaluation module is used to evaluate the regulation effect according to the feedback data uploaded by the edge devices to generate an effect evaluation result, and adjust the hierarchical differential regulation model according to the effect evaluation result.

8. The distributed resource control system based on cross-domain dynamic coupling according to any one of claims 1-6, characterized in that The cloud-edge collaborative synchronization module further includes: The source-load prediction module is used to perform source-load prediction according to environmental data, weather data, and historical data. The positioning and pushing module is used to locate the fault information and push the fault information in real time.

9. A distributed resource control method based on cross-domain dynamic coupling, characterized in that, including: Analyze the variation laws and distribution characteristics of the collected distributed resource operation data in the spatio-temporal dimension through the spatio-temporal dynamic coupling analysis module to obtain the spatio-temporal distribution of distributed resources. Based on the spatio-temporal distribution of distributed resources, construct an adjustable resource scheduling optimization model, and input the actual resource data to be analyzed into the adjustable resource scheduling optimization model to generate a resource optimization scheduling plan; Generate a multi-level differential regulation model through the multi-level differential regulation module according to the resource optimization scheduling plan and the hierarchical and grouped architecture, so as to adopt different regulation strategies for the regional power grid, distribution network and microgrid in the hierarchical and grouped architecture; Generate regulation indicators according to the regulation strategy through the cloud-edge collaborative synchronization module, and send the regulation indicators to the edge devices; Among them, the multi-level differential regulation module includes: The regional power grid regulation module is used to generate an economic and low-carbon operation regulation strategy based on the resource optimization scheduling plan and the deep neural network model; The distribution network regulation module is used to establish a task index decomposition system based on the resource optimization scheduling plan, and adopt a multi-round response mechanism for the day-ahead and within-day to roll-execute the tasks in the task index decomposition system, and monitor the accuracy of the executed tasks to generate an intensive management strategy; The microgrid regulation module is used to carry out line power flow monitoring and analyze the line operation conditions. When the mutual assistance conditions are met, generate a mutual assistance and partition autonomy strategy according to the resource optimization scheduling plan.

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