Distributed Energy Interconnection Method and System for Power System

Abstract

The present invention is applicable to the technical field of energy management, and provides a distributed energy interconnection method and system for a power system. The system includes: a status information reading module, an energy status identification module, a dynamic energy interconnection network construction module, an islanding switching instruction module, and an operation data collection module. The multi-level dynamic regulation and closed-loop optimization mechanism of this method not only realizes the flexible adaptation ability of the distributed energy system, but also improves the stability and efficiency of long-term operation through continuous feedback and adjustment, laying a foundation for the sustainable development of the power network. It is particularly applicable to the requirements of modern power systems with extensive and diverse energy distributions, and helps to promote the efficient utilization of energy resources and the intelligent upgrade of the system. Generally speaking, this method provides a comprehensive, scientific and reliable solution for the construction of power systems, and has remarkable economy, safety and scalability.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy management, and particularly relates to a method and system for distributed energy interconnection in a power system. Background Art

[0002] Energy management technology is an interdisciplinary comprehensive technical field, involving the entire process of energy production, transmission, distribution, storage, and efficient utilization. It aims to optimize energy use efficiency, reduce energy consumption, and minimize environmental impact through technical means, while ensuring the stability and security of energy supply. Energy management technology is of great significance in addressing global challenges such as energy security, climate change, and resource limitations, and is a key support for promoting the modern energy system towards high efficiency, intelligence, and sustainability.

[0003] In the face of central power grid failures, existing technologies often lack effective support for the independent operation capabilities of distributed nodes. This makes it highly likely that local faults in the power grid can trigger large-scale power outages. Moreover, due to the limitations of monitoring means, the identification and handling of power grid anomalies often lag, and it is impossible to detect abnormal trends in the early stage of faults and take effective isolation measures, further exacerbating the spread of faults and making it difficult to meet the requirements of modern smart grids for flexibility and adaptability. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for distributed energy interconnection in a power system, aiming to solve the technical problems existing in the prior art as identified in the background art.

[0005] The present invention is implemented as follows. A method for distributed energy interconnection in a power system, the method comprising:

[0006] Reading the real-time status information obtained by monitoring a number of distributed nodes within a regional scope;

[0007] Obtaining the historical data monitored by all distributed nodes, combining the real-time data, establishing an operating load prediction model, and identifying potential supply-demand imbalances;

[0008] Based on the prediction results, combining the geographical locations, load demands, and energy supply capabilities of each distributed node, dynamically constructing an energy interconnection network, establishing connections through a networking mechanism based on the load status of each distributed node, performing energy distribution, and real-time monitoring the network status through edge computing distributed nodes to adjust the energy interconnection network;

[0009] Real-time detecting the operating status data of the central power grid, determining whether there are abnormal indicators, and at the same time analyzing whether the abnormal indicators will cause the power grid to fail. If a failure signal is identified, an islanding switch instruction is sent to all distributed nodes to disconnect the connection between each distributed node and the central power grid, and each distributed node enters island autonomous operation;

[0010] Collect operation data in real time and evaluate the implementation effect of the solution. Combine the feedback results and the latest status information to dynamically adjust the operation strategies of the energy Internet and distributed nodes, forming a self-optimizing closed loop.

[0011] As a further solution of the present invention, obtaining the historical data monitored by all distributed nodes, combining with the real-time data, establishing an operation load prediction model, and identifying potential supply-demand imbalances specifically include:

[0012] Read the collected real-time data and historical status data;

[0013] Based on the real-time data and historical status data, establish a load prediction model, and train the load prediction model through the historical status data;

[0014] Combine the real-time data, and through the load prediction model, predict the future energy supply-demand situation of each distributed node, and identify potential supply-demand imbalances.

[0015] As a further solution of the present invention, predicting the future energy supply-demand situation of each distributed node specifically is:

[0016] ;

[0017] Among them, represents the output power of the distributed energy distributed node at the future moment ; is the conversion efficiency of the distributed node ; represents the supply quantity of the input energy of the distributed node at the moment , including photovoltaic power generation, wind power generation, energy storage power generation, fuel power generation and hydropower generation, represents the operation state of the distributed node ;

[0018] ;

[0019] ;

[0020] ;

[0021] ;

[0022] Among them, is the energy supply-demand difference at time ; represents the total number of power generation distributed nodes, is the total number of load distributed nodes, is the power consumption demand of the th distributed load distributed node, is a periodic load, is a random dynamic load, is an external event correction factor;

[0023] is the average value of the baseline load, 、 are the coefficients of the Fourier series, is the harmonic order of the Fourier series, representing different periodic components of the load fluctuation, is the truncation order of the Fourier series, is the order of the Fourier term, is the period length, is the distributed node number of load devices, is the device at time rated power, is the device at time switching state, is the device running, is the device turned off.

[0024] As a further solution of the present invention, based on the prediction results, an energy interconnection network is dynamically constructed. Based on the load status of each distributed node, connections are established through a networking mechanism for energy distribution, and the network status is monitored in real time by edge computing distributed nodes to adjust the energy interconnection network, specifically including:

[0025] Based on the energy supply and demand differences of each distributed node, for the distributed nodes with energy shortages, find the closest distributed nodes with energy surpluses for matching. The supply and demand matching is based on:

[0026] ;

[0027] Among them, represents the energy exchange volume between distributed node and distributed node , is the distributed node and distributed node communication delay between;

[0028] After the distributed nodes are matched, connections between the distributed nodes are established to form a preliminary energy flow path network;

[0029] Based on the constructed preliminary energy flow path network, preliminary energy distribution is carried out according to the supply and demand differences of the distributed nodes;

[0030] Allocate specific energy flows among distributed nodes, and comprehensively consider the losses in the energy transmission process to obtain the actually arrived energy;

[0031] Monitor the network status in real time through edge computing, and adjust the energy interconnection network and energy distribution plan according to the real-time situation.

[0032] As a further solution of the present invention, the allocation of specific energy flows among the distributed nodes and the comprehensive consideration of the losses in the energy transmission process to obtain the actually arrived energy are specifically as follows:

[0033] ;

[0034] ;

[0035] Among them, represents the arrived energy after combining the losses, represents the energy flow allocated among the distributed nodes, represents the transmission loss among the distributed nodes, represents the margin of the distributed node ; represents the demand of the distributed node .

[0036] As a further solution of the present invention, the real-time detection of the operation status data of the central power grid, the judgment of whether there are abnormal indicators, and the analysis of whether the abnormal indicators will cause the power grid to fail. If a failure signal is identified, an island switching instruction is sent to all distributed nodes to disconnect the connection between each distributed node and the central power grid, and each distributed node enters island autonomy, specifically including:

[0037] Obtain the operation status data from the central power grid in real time and input it for analysis, and compare the operation status data with the preset operation threshold to detect the abnormal trend and judge whether there are abnormal indicators;

[0038] Simulate the propagation path of the abnormal fault, evaluate the impact of the abnormality on the frequency, and set the failure determination conditions according to the power grid operation rules, that is, the system frequency is lower than the critical value and the system frequency is higher than the critical value, and evaluate the abnormal impact level of each distributed node;

[0039] Set the abnormal level threshold, and make a judgment based on the evaluated abnormal impact level. If it exceeds the abnormal level threshold, a switching to the island mode instruction is sent to the corresponding distributed node;

[0040] Disconnect the connection between the distributed node that receives the island mode instruction and the central power grid, so that the distributed node enters island autonomy.

[0041] Another object of the present invention is to provide a distributed energy interconnection system for a power system, the system comprising:

[0042] A status information reading module for reading real-time status information obtained by monitoring a number of distributed nodes within a region;

[0043] An energy status identification module for obtaining historical data monitored by all distributed nodes, combining real-time data, establishing an operating load prediction model, and identifying potential supply-demand imbalances;

[0044] A dynamic energy interconnection network construction module for dynamically constructing an energy interconnection network based on the prediction results, combining the geographical locations, load demands, and energy supply capabilities of each distributed node, establishing connections through a networking mechanism based on the load status of each distributed node, performing energy distribution, and real-time monitoring the network status through edge computing distributed nodes to adjust the energy interconnection network;

[0045] An islanding switching instruction module for real-time detecting the operating status data of the central power grid, judging whether there are abnormal indicators, and at the same time analyzing whether the abnormal indicators will cause the power grid to fail. If a failure signal is identified, an islanding switching instruction is sent to all distributed nodes to disconnect the connection between each distributed node and the central power grid, and each distributed node enters island autonomous operation;

[0046] An operating data collection module for real-time collecting operating data and evaluating the implementation effect of the scheme. Combining the feedback results and the latest status information, dynamically adjusting the energy interconnection network and the operating strategies of distributed nodes to form a self-optimizing closed loop.

[0047] The beneficial effects of the present invention are:

[0048] Through the coordinated operation of steps such as real-time monitoring of distributed nodes, load prediction, dynamic networking, island operation, and self-optimizing closed loop, this method demonstrates powerful comprehensive beneficial effects. First of all, this method significantly improves the intelligent level of the system. Through real-time data collection and advanced prediction models, it accurately identifies energy shortages and surpluses, provides a scientific decision-making basis for subsequent resource allocation, and avoids resource waste and supply-demand imbalance problems in traditional static power systems. Secondly, dynamically constructing the energy interconnection network can effectively minimize transmission losses and resource scheduling costs, and through edge computing, real-time monitoring and adjustment are achieved, ensuring the efficiency of energy distribution and the reliability of system operation. In addition, the island operation mechanism ensures the independent power supply capacity of local nodes through rapid switching in case of faults, preventing the further spread of faults, which greatly improves the resilience and security of the system.

[0049] The multi-level dynamic regulation and closed-loop optimization mechanism of this method not only realizes the flexible adaptability of distributed energy systems, but also improves the stability and efficiency of long-term operation through continuous feedback and adjustment, laying a foundation for the sustainable development of power grids. It is particularly suitable for the requirements of modern power systems with widespread and diverse energy distributions, and helps to promote the efficient utilization of energy resources and the intelligent upgrading of systems. Generally speaking, this method provides a comprehensive, scientific and reliable solution for the construction of power systems, with significant economic, safety and scalability characteristics. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a flowchart of the distributed energy interconnection method for the power system provided by an embodiment of the present invention;

[0051] Figure 2 It is a flowchart of establishing and operating a load prediction model to identify potential supply-demand imbalances provided by an embodiment of the present invention;

[0052] Figure 3 It is a flowchart of conducting energy distribution, and real-time monitoring of the network status through edge computing distributed nodes to adjust the energy interconnection network provided by an embodiment of the present invention;

[0053] Figure 4 It is a flowchart of disconnecting the connection between each distributed node and the central power grid, and each distributed node entering island autonomous operation provided by an embodiment of the present invention;

[0054] Figure 5 It is a structural block diagram of the distributed energy interconnection system for the power system provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0055] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0056] It can be understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish the first element from another element. For example, without departing from the scope of this application, the first xx script may be called the second xx script, and similarly, the second xx script may be called the first xx script.

[0057] Figure 1 It is a flowchart of the distributed energy interconnection method for the power system provided by an embodiment of the present invention, as Figure 1 shown, the method includes:

[0058] S100. Read the real-time status information obtained from the monitoring of several distributed nodes within the reading area;

[0059] S200. Obtain the historical data monitored by all distributed nodes, combine the real-time data, establish an operating load prediction model, and identify potential supply-demand imbalances;

[0060] This step can generate a complete time-series data set by reading real-time data and historical status data, covering the energy supply-demand status of distributed nodes under various operating conditions. These data are processed through a multi-dimensional feature extraction algorithm, including parameters such as the input power of distributed nodes, load characteristics, and environmental impacts. Subsequently, based on these data, a high-precision load prediction model is constructed to clearly describe the dynamic energy supply capacity of distributed nodes.

[0061] To improve the accuracy of the prediction model, historical status data is used to continuously train and optimize the model. During the training process, the model will automatically capture the seasonal, periodic, and random characteristics of load demand.

[0062] After predicting the future supply-demand situation of each node, the system will further calculate the overall energy supply-demand balance status. Through this calculation, the system can quickly identify possible supply-demand imbalance points (such as energy shortages or surpluses), and combined with the correlation and geographical distribution between nodes, propose optimization strategies. This process provides data support and guiding directions for dynamic networking and energy allocation in subsequent steps.

[0063] This step greatly improves the forward-looking and adaptability of the system by reading multi-dimensional data and constructing an accurate load prediction model. Through the training process of combining historical data, the model can perform in-depth learning on load fluctuations at different time scales, ensuring high-precision and high-reliability predictions. Compared with the static planning of traditional power systems, this method can identify potential energy shortages or surpluses in real time in a dynamic environment, thus providing a scientific basis for subsequent energy distribution and networking. In addition, based on the periodic analysis of Fourier decomposition, the long-term change trend of distributed nodes can be captured, and through the integration of dynamic load and external correction factors, it can quickly respond to the impacts of short-term random fluctuations and special events. This multi-level prediction method enables the system to regulate more efficiently in the face of complex energy supply-demand conditions, significantly improving the operating stability and resource utilization efficiency of the entire power system.

[0064] As Figure 2 shown, the obtaining of the historical data monitored by all distributed nodes, combining the real-time data, establishing an operating load prediction model, and identifying potential supply-demand imbalances specifically includes:

[0065] S210. Read the collected real-time data and historical status data;

[0066] S220, establish a load forecasting model based on real-time data and historical status data, and train the load forecasting model with the historical status data;

[0067] S230, combine the real-time data, and through the load forecasting model, predict the future energy supply and demand situation of each distributed node, and identify potential supply-demand imbalances.

[0068] In this step, the prediction of the future energy supply and demand situation of each distributed node is specifically:

[0069] ;

[0070] Among them, represents the output power of the distributed energy distributed node at the future moment ; is the conversion efficiency of the distributed node ; represents the supply of the input energy of the distributed node at the moment , including photovoltaic power generation, wind power generation, energy storage power generation, fuel power generation and hydropower generation; represents the operating state of the distributed node ;

[0071] ;

[0072] ;

[0073] ;

[0074] ;

[0075] Among them, is the energy supply-demand difference at time ; represents the total number of power generation distributed nodes, is the total number of load distributed nodes, is the th electricity demand of the th distributed load distributed node, is the periodic load, is the random dynamic load,

[0076] is the average value of the baseline load, , are the coefficients of the Fourier series, is the harmonic order of the Fourier series, representing different periodic components of the load fluctuation, is the truncation order of the Fourier series, is the order of the Fourier term, is the period length, is the distributed node the number of load devices, is the device at time the rated power, is the device at time the switch state, is the device running, is the device off.

[0077] S300, based on the prediction results, combines the geographical locations, load demands, and energy supply capabilities of each distributed node to dynamically construct an energy interconnection network. Based on the load status of each distributed node, it establishes connections through a networking mechanism, conducts energy distribution, and real-time monitors the network status through edge computing distributed nodes to adjust the energy interconnection network;

[0078] This step uses an optimization model to find the best match between energy shortage nodes and energy surplus nodes according to the energy supply and demand differences of each distributed node, and constructs a preliminary path network for energy flow with the goal of minimizing the transmission cost. In the process, actual constraint conditions such as the energy interaction volume and communication delay between nodes are considered, so that the matching scheme is not only optimal in energy utilization, but also meets the system requirements in terms of transmission efficiency and response speed. Through this dynamic matching mechanism, the value of the energy surplus of each node can be fully utilized, resource waste is avoided, and the supply-demand balance efficiency is improved.

[0079] After completing node matching and path construction, the system further distributes specific energy flows according to the supply-demand differences, and comprehensively considers the losses in the energy transmission process to obtain the actually arrived energy. This link is particularly important for distributed energy interconnection because in actual transmission, energy is inevitably affected by line losses. Through accurate modeling and dynamic adjustment of the losses, the system can accurately calculate the amount of actually arrived energy, thus ensuring the reliability and effectiveness of the distribution scheme. In addition, during the distribution process, the demands of energy shortage nodes are given priority, while over-consumption of energy surplus nodes is avoided, achieving a dynamic balance of global resource utilization.

[0080] During the operation of the network, the system monitors the state changes of nodes and the network in real time through edge computing, and dynamically adjusts the topological structure of the energy Internetwork and the energy distribution scheme. This real-time monitoring based on edge computing greatly shortens the response time and improves the system's adaptability to emergencies. For example, when a node experiences a sudden increase in load resulting in a short-term energy shortage, the system can immediately re-match the surplus nodes and adjust the energy flow direction to ensure the stable operation of the load. This dynamic adjustment ability enables the network to have strong self-adaptability and robustness, and can cope with the uncertainties commonly existing in distributed energy systems.

[0081] This step is based on node matching and the construction of an energy flow path network. This step can minimize the time cost and transmission loss of resource scheduling, thereby achieving efficient energy distribution. Secondly, by comprehensively considering the communication delay, energy transmission loss, and actual supply-demand differences between nodes, this step can ensure the reliability and economy of the operation of the distributed energy system. In addition, by introducing edge computing technology, the system has the ability of real-time monitoring and dynamic adjustment, and the response speed to environmental changes and node state changes has been greatly improved, which provides guarantee for the safe operation of the distributed energy system in abnormal situations. More importantly, this step also provides a prerequisite for the long-term optimization of the energy Internetwork, forming a continuous improvement closed-loop operation mechanism. This efficient, flexible, and intelligent energy interconnection method not only improves the operation efficiency of the distributed energy system, but also provides a model for the development of more complex energy systems in the future.

[0082] As Figure 3 shown, based on the prediction results, an energy Internetwork is dynamically constructed. Based on the load status of each distributed node, connections are established through a networking mechanism for energy distribution, and the network status is monitored in real time through edge computing distributed nodes to adjust the energy Internetwork. Specifically, it includes:

[0083] S310. Based on the energy supply-demand differences of each distributed node, for the distributed nodes with energy shortages, find the closest distributed nodes with energy surpluses for matching. The supply-demand matching is based on:

[0084] ;

[0085] wherein, represents the energy exchange volume between distributed node and distributed node , is the communication delay between distributed node and distributed node ;

[0086] S320. After the distributed nodes are matched, establish connections between the distributed nodes to form a preliminary energy flow path network;

[0087] S330, Based on the initially constructed energy flow path network, conduct preliminary energy distribution according to the supply-demand differences of distributed nodes;

[0088] S340, Allocate specific energy flows between distributed nodes, and comprehensively consider the losses during the energy transmission process to obtain the actually arriving energy;

[0089] S350, Real-time monitor the network status through edge computing, and adjust the energy interconnection network and energy distribution plan according to the real-time situation.

[0090] In this step, when allocating specific energy flows between distributed nodes and comprehensively considering the losses during the energy transmission process to obtain the actually arriving energy, specifically:

[0091] ;

[0092] ;

[0093] wherein, represents the arriving energy after considering the losses, represents the energy flow allocated between distributed nodes, represents the transmission loss between distributed nodes, represents the distributed node margin, represents the distributed node demand.

[0094] S400, Real-time detect the operation status data of the central power grid, determine whether there are abnormal indicators, and at the same time analyze whether the abnormal indicators will cause the power grid to fail. If a failure signal is identified, send an islanding switchover instruction to all distributed nodes to disconnect the connection between each distributed node and the central power grid, and each distributed node enters island autonomous operation;

[0095] This step can quickly discover possible abnormal trends in the system by obtaining operation status data from the central power grid in real time and inputting these data into the analysis module for comparison with the preset operation thresholds. The preset operation thresholds include the normal ranges of key operation parameters such as voltage, frequency, and current. When some operation status indicators start to show a trend of deviating from the normal range, the system will immediately issue a warning signal and further analyze whether these abnormalities may develop into faults affecting the power grid stability. Compared with the traditional method relying on manual monitoring, this real-time data monitoring and analysis significantly improves the efficiency and sensitivity of abnormal detection.

[0096] After detecting an abnormality, the system will simulate the propagation path of the abnormal fault and evaluate its impact on the frequency, stability and distributed nodes of the entire power grid. Frequency is one of the important signs of power grid operation. When the system frequency is lower than or higher than the critical value, it often indicates that the power grid has failed or is about to fail. According to the power grid operation rules, the system sets the failure judgment conditions and further evaluates the severity of the fault by analyzing the frequency changes during the abnormal propagation process. At the same time, the abnormal impact level of each distributed node is evaluated. This level reflects the possibility of each node being affected by the abnormality and the degree of impact on the node operation after the abnormality is affected. For example, nodes close to the fault source usually have a higher abnormal impact level, while nodes far away from the fault source or with a higher independent operation capability have a relatively low impact level.

[0097] Subsequently, the system sets a threshold for the abnormal level and makes an island switching judgment based on the abnormal impact level of each distributed node. When the impact level of a node exceeds the threshold, the system will automatically issue an island operation mode switching instruction to the node, instructing it to disconnect from the central power grid and enter island autonomous operation. The island autonomous mode enables the node to independently maintain power supply using its own distributed energy supply capacity when disconnected from the external power grid, thereby ensuring the stability and safety of power supply in the local area. At the same time, orderly island switching can effectively prevent faults from spreading to the entire network, avoiding chain reactions that cause network-wide failures.

[0098] This step combines real-time monitoring with abnormal analysis to achieve rapid perception and accurate judgment of the power grid status, greatly improving the safety and reliability of power grid operation. The system can identify potential risks in the early stages of a fault and take isolation measures in a timely manner to avoid more serious consequences due to delayed response. Secondly, based on the simulation and impact assessment of the abnormal fault propagation path, this step can conduct an in-depth analysis of the scope, extent and impact of the fault to ensure that the decision on island switching is well-founded and efficient. This precise assessment avoids the waste of resources that may be caused by excessive switching, while ensuring the operational stability of necessary nodes after switching.

[0099] In addition, the island autonomous mode not only ensures the independent power supply ability of nodes, but also provides important support for the flexibility and resilience of distributed energy systems. Even in the case of the failure of the central power grid, the island mode can effectively maintain local power supply and reduce the adverse effects on users caused by power grid failures. This operating mode is particularly suitable for modern energy systems with a high proportion of distributed energy and wide user distribution, and conforms to the overall trend of the intelligent and distributed new power system. More importantly, through the setting of a closed-loop control mechanism, this step can effectively manage the island operation and the restoration of the connection to the central power grid in the subsequent stage, realizing the self-optimization and overall safety guarantee of system operation. Therefore, S4 is not only the core link of fault defense in the new power system, but also provides a solution for the efficient operation of large-scale distributed energy networks in the future that can be used for reference.

[0100] As Figure 4 shown, it is to detect the operation status data of the central power grid in real time, judge whether there are abnormal indicators, and at the same time analyze whether the abnormal indicators will lead to the failure of the power grid. If a failure signal is identified, an island switching instruction is sent to all distributed nodes to disconnect the connection between each distributed node and the central power grid, and each distributed node enters island autonomy, which specifically includes:

[0101] S410, obtain the operation status data from the central power grid in real time, input it for analysis, and compare the operation status data with the preset operation threshold to detect abnormal trends and judge whether there are abnormal indicators;

[0102] S420, simulate the propagation path of abnormal faults, evaluate the impact of the abnormality on the frequency, set the failure determination conditions according to the power grid operation rules, that is, the system frequency is lower than the critical value and the system frequency is higher than the critical value, and evaluate the abnormal impact level of each distributed node;

[0103] S430, set the abnormal level threshold, judge based on the evaluated abnormal impact level, and if it exceeds the abnormal level threshold, send an instruction to switch to the island mode to the corresponding distributed node;

[0104] S440, disconnect the connection between the distributed node that receives the island mode instruction and the central power grid, so that the distributed node enters island autonomy.

[0105] S500, collect operation data in real time and evaluate the implementation effect of the scheme. Combining the feedback results and the latest status information, dynamically adjust the operation strategies of the energy interconnection network and distributed nodes to form a self-optimizing closed loop.

[0106] Figure 5 It is the structural block diagram of the distributed energy interconnection system of the power system provided by the embodiment of the present invention. As Figure 5 shown, the system includes:

[0107] The status information reading module 100 is used to read the real-time status information obtained from the monitoring of several distributed nodes within the area range;

[0108] The energy status identification module 200 is used to obtain the historical data monitored by all distributed nodes, combine the real-time data, establish an operating load prediction model, and identify potential supply-demand imbalances;

[0109] The dynamic energy interconnection network construction module 300 is used to dynamically construct an energy interconnection network based on the prediction results, combine the geographical locations, load demands, and energy supply capabilities of each distributed node, establish connections through a networking mechanism based on the load status of each distributed node, perform energy distribution, and monitor the network status in real-time through edge computing distributed nodes to adjust the energy interconnection network;

[0110] The islanding switching instruction module 400 is used to detect the operating status data of the central power grid in real-time, determine whether there are abnormal indicators, and at the same time analyze whether the abnormal indicators will cause the power grid to fail. If a failure signal is identified, an islanding switching instruction is sent to all distributed nodes to disconnect the connection between each distributed node and the central power grid, and each distributed node enters island autonomous operation;

[0111] The operation data collection module 500 is used to collect operation data in real-time and evaluate the implementation effect of the solution. Combining the feedback results and the latest status information, dynamically adjust the operation strategies of the energy interconnection network and distributed nodes to form a self-optimizing closed loop.

[0112] It should be understood that although the steps in the flowcharts of the embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in each embodiment may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or sub-steps or stages of other steps.

[0113] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in this application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0114] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0115] The above embodiments only represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention should be subject to the appended claims.

[0116] The above is only the preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A distributed energy interconnection method for a power system, characterized in that, The method includes: Reading the real-time status information monitored by several distributed nodes within the area range; Obtaining the historical data monitored by all distributed nodes, combining with the real-time data, establishing an operating load prediction model, and identifying potential supply-demand imbalances; Based on the prediction results, combining the geographical locations, load demands, and energy supply capabilities of each distributed node, dynamically constructing an energy interconnection network, establishing connections through a networking mechanism based on the load status of each distributed node, performing energy distribution, and real-time monitoring the network status through edge computing distributed nodes to adjust the energy interconnection network; Real-time detecting the operating status data of the central power grid, judging whether there are abnormal indicators, and at the same time analyzing whether the abnormal indicators will cause the power grid to fail. If a failure signal is identified, an islanding switching instruction is sent to all distributed nodes to disconnect the connection between each distributed node and the central power grid, and each distributed node enters island autonomous operation; Real-time collecting operation data, evaluating the implementation effect of the solution, combining the feedback results and the latest status information, dynamically adjusting the energy interconnection network and the operation strategy of distributed nodes to form a self-optimizing closed loop; Among them, the dynamically constructing an energy interconnection network based on the prediction results, establishing connections through a networking mechanism based on the load status of each distributed node, performing energy distribution, and real-time monitoring the network status through edge computing distributed nodes to adjust the energy interconnection network specifically includes: Based on the energy supply-demand differences of each distributed node, for the distributed nodes with energy shortages, finding the nearest distributed nodes with energy surpluses for matching, and the supply-demand matching is based on: ； Among them, represents minimizing the weighted sum of the energy exchange volume and communication delay of distributed nodes, represents the distributed node and the distributed node of the energy exchange volume, is the communication delay between the distributed node and the distributed node ; After the distributed node matching is completed, establish connections between the distributed nodes to form a preliminary energy flow path network; Based on the constructed preliminary energy flow path network, perform preliminary energy distribution according to the supply-demand differences of the distributed nodes; Allocate specific energy flows between distributed nodes, and comprehensively consider the losses in the energy transmission process to obtain the actually arrived energy; Real-time monitor the network status through edge computing, and adjust the energy interconnection network and the energy distribution scheme according to the real-time situation; The specifically allocating specific energy flows between distributed nodes and comprehensively considering the losses in the energy transmission process to obtain the actually arrived energy is specifically: ； ； Among them, represents the energy arriving after the coupling loss, represents the energy flow distributed among the distributed nodes, represents the transmission loss among the distributed nodes, represents the distributed node margin, represents the distributed node demand.

2. The method according to claim 1, wherein The obtaining the historical data monitored by all distributed nodes, combining with the real-time data, establishing an operating load prediction model, and identifying potential supply-demand imbalances specifically includes: Reading the collected real-time data and historical status data; Based on the real-time data and historical status data, establish a load prediction model, and train the load prediction model with the historical status data; Combining with the real-time data, through the load prediction model, predict the future energy supply-demand situations of each distributed node, and identify potential supply-demand imbalances.

3. The method according to claim 2, wherein The specifically predicting the future energy supply-demand situations of each distributed node is: ； Among them, represents the distributed node of distributed energy at a future moment output power, is the conversion efficiency of the distributed node ; represents the supply of input energy of the distributed node at the moment , including photovoltaic power generation, wind power generation, energy storage power generation, fuel power generation and hydropower generation, represents the operating state of the distributed node ; ； ； ； ； Among them, is the time of the energy supply-demand difference, represents the total number of power generation distributed nodes, is the total number of load distributed nodes, is the th power consumption demand of the load distributed node of the th distributed load, is the periodic load, is the external event correction factor; is the average value of the baseline load, , is the coefficient of the Fourier series, is the harmonic order of the Fourier series, representing different periodic components of the load fluctuation, is the truncation order of the Fourier series, is the order of the Fourier term, is the period length, is the distributed node the number of load devices, is the device at time the rated power, is the device at time the switch state, is the device running, is the device off.

4. The method according to claim 1, characterized in that The method for real-time detecting the operation state data of the central power grid, judging whether there are abnormal indicators, and simultaneously analyzing whether the abnormal indicators will cause the power grid to fail. If a failure signal is identified, an islanding switching instruction is sent to all distributed nodes to disconnect each distributed node from the central power grid, and each distributed node enters islanding autonomy, specifically including: Real-time acquiring operation state data from the central power grid and inputting it for analysis, comparing the operation state data with a preset operation threshold value, detecting abnormal trends, and judging whether there are abnormal indicators; Simulating the propagation path of abnormal faults, evaluating the influence of the abnormality on the frequency, setting failure determination conditions according to the power grid operation rules, that is, the system frequency is lower than the critical value or the system frequency is higher than the critical value, and evaluating the abnormal influence level of each distributed node; Setting an abnormal level threshold value, judging based on the evaluated abnormal influence level. If the abnormal level threshold value is exceeded, an instruction to switch the corresponding distributed node to the islanding mode is sent; Disconnecting the distributed node that receives the islanding mode instruction from the central power grid, so that the distributed node enters islanding autonomy.

5. The method according to claim 1, wherein The system for implementing the distributed energy interconnection method of the power system includes: A status information reading module, which is used to read the real-time status information obtained by monitoring a number of distributed nodes within a regional scope; An energy status identification module, which is used to obtain the historical data monitored by all distributed nodes, combine the real-time data, establish an operation load prediction model, and identify potential supply-demand imbalances; A dynamic energy interconnection network construction module, which is used to dynamically construct an energy interconnection network based on the prediction results, combine the geographical locations, load demands, and energy supply capabilities of each distributed node, establish connections through a networking mechanism based on the load status of each distributed node, perform energy distribution, and real-time monitor the network status through edge computing distributed nodes to adjust the energy interconnection network; An islanding switching instruction module, which is used to real-time detect the operation state data of the central power grid, judge whether there are abnormal indicators, and simultaneously analyze whether the abnormal indicators will cause the power grid to fail. If a failure signal is identified, an islanding switching instruction is sent to all distributed nodes to disconnect each distributed node from the central power grid, and each distributed node enters islanding autonomy; An operation data collection module, which is used to real-time collect operation data, evaluate the implementation effect of the scheme, combine the feedback results and the latest status information, dynamically adjust the energy interconnection network and the operation strategy of the distributed nodes, and form a self-optimizing closed loop.

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