State monitoring and hierarchical optimization method and device, computer equipment and storage medium

Abstract

The invention belongs to the field of power optimization, and relates to a state monitoring and hierarchical optimization method and device, computer equipment and a storage medium, and the method is based on multiple energy flows of a power distribution network, and comprises the steps: obtaining real-time data of each node in a power grid; processing and analyzing the real-time data to obtain state information of each node in the power grid; analyzing load characteristics of each node in the power grid according to the state information; selecting and configuring an energy storage system according to the analysis result of the load characteristics of each node in the power grid; formulating a hierarchical optimization strategy according to the energy storage system; and evaluating and optimizing the power grid after the hierarchical optimization strategy is implemented. By implementing the embodiment, the operation efficiency and the energy utilization efficiency of the power grid are improved, the flexibility and the reliability of the power grid are remarkably enhanced, and powerful support is provided for intelligent management of a power distribution network.

Description

Technical Field

[0001] The present invention relates to the technical field of power optimization, and in particular to a method, device, computer equipment and storage medium for state monitoring and hierarchical optimization. Background Art

[0002] Although the existing state monitoring methods based on multi-energy flows in distribution networks have achieved real-time tracking and monitoring of the grid operation status to a certain extent, they still have many defects, especially in terms of grid operation efficiency, energy utilization efficiency, flexibility and reliability.

[0003] In terms of operational efficiency, current condition monitoring methods often focus on data collection and display, lacking in-depth data mining and analysis. This makes it difficult to promptly identify and optimize potential grid issues, impacting overall operational efficiency. Furthermore, inefficient energy utilization is a major issue. Some older equipment has high energy consumption and low efficiency, and existing monitoring methods fail to effectively guide the upgrade and replacement of these devices.

[0004] In terms of flexibility, current monitoring methods are insufficiently responsive to rapid changes in the grid structure, making it difficult to adapt to new situations such as the large-scale integration of new energy sources and the diversification of user demands. This limits the grid's flexibility in resource allocation and fault recovery.

[0005] In terms of reliability, existing condition monitoring methods lack the ability to provide early warning of potential failures, often requiring post-fault analysis only after a failure has occurred. This significantly reduces grid reliability. Furthermore, the ability to respond to emergencies such as extreme weather and external damage needs to be improved. Summary of the Invention

[0006] In order to solve the above technical problems, the present invention provides a condition monitoring and hierarchical optimization method, which adopts the following technical solutions, including:

[0007] Obtain real-time data of each node in the power grid;

[0008] Processing and analyzing the real-time data to obtain status information of each node in the power grid;

[0009] Analyzing the load characteristics of each node in the power grid based on the status information;

[0010] Select and configure energy storage systems based on the analysis of load characteristics of each node in the power grid;

[0011] Formulate a hierarchical optimization strategy based on the energy storage system;

[0012] Evaluate and optimize the power grid after implementing the hierarchical optimization strategy.

[0013] Preferably, the step of obtaining real-time data of each node in the power grid specifically includes:

[0014] Acquiring the real-time data measured by sensors placed at each node in the power grid;

[0015] The real-time data is preprocessed.

[0016] Preferably, the step of processing and analyzing the real-time data to obtain status information of each node in the power grid specifically includes:

[0017] Performing data fusion on the real-time data to form a complete and consistent view of the power grid status;

[0018] The real-time data after data fusion is compared with a preset normal state threshold value to obtain the state information of each node in the power grid and determine whether the state of each node in the power grid is normal.

[0019] Preferably, the step of analyzing the load characteristics of each node in the power grid according to the state information specifically includes:

[0020] According to the power consumption characteristics of the load, the load is divided into different types;

[0021] Use historical load data to analyze load change patterns and trends.

[0022] Preferably, the step of selecting and configuring the energy storage system based on the result of analyzing the load characteristics of each node in the power grid specifically includes:

[0023] Analyze the capacity, power, and response time parameters of the energy storage system based on the load characteristics and needs of the power grid;

[0024] Determine the installation location and capacity allocation of the energy storage system based on the structure and load distribution of the power grid.

[0025] Preferably, the step of formulating a hierarchical optimization strategy according to the energy storage system specifically includes:

[0026] According to the energy storage system, the power distribution network is divided into a bottom-level power distribution network, a middle-level power distribution network and a top-level power distribution network;

[0027] Optimization strategies for the bottom-level power distribution network, the middle-level power distribution network, and the top-level power distribution network are formulated respectively.

[0028] Preferably, the step of evaluating and optimizing the power grid after implementing the hierarchical optimization strategy specifically includes:

[0029] Select evaluation indicators based on the characteristics and needs of the power grid;

[0030] Collect grid data after implementing the hierarchical optimization strategy, and perform preprocessing and analysis;

[0031] Formulate and implement optimization measures based on pre-processed and analyzed power grid data.

[0032] In order to solve the above technical problems, the present invention further provides a condition monitoring and hierarchical optimization device, which adopts the following technical solution, including:

[0033] Acquisition module, used to obtain real-time data of each node in the power grid;

[0034] A processing module, configured to process the real-time data and obtain status information of each node in the power grid;

[0035] an analysis module, configured to analyze the load characteristics of each node in the power grid based on the status information;

[0036] A configuration module is used to select and configure the energy storage system based on the results of analyzing the load characteristics of each node in the power grid;

[0037] A formulation module, for formulating a hierarchical optimization strategy according to the energy storage system;

[0038] The optimization module is used to evaluate and optimize the power grid after implementing the hierarchical optimization strategy.

[0039] In order to solve the above technical problems, the present invention also provides a computer device, which adopts the technical solution described below, including a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the above-mentioned state monitoring and hierarchical optimization method when executing the computer-readable instructions.

[0040] In order to solve the above technical problems, the present invention also provides a computer-readable storage medium, which adopts the technical solution described below, wherein the computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps of the above-mentioned state monitoring and hierarchical optimization method are implemented.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] (1) Using sensors to obtain key data such as voltage, current, and power at each node in the power grid in real time, and using data processing and analysis algorithms to deeply mine these real-time data to accurately obtain the operating status information of each node in the power grid;

[0043] (2) Based on the acquired status information, further analyze the load characteristics of each node in the power grid, including load volatility, peak and valley periods, and load types, to provide a scientific basis for the subsequent configuration of energy storage systems. Based on the load characteristic analysis results, targeted energy storage systems, such as battery energy storage and supercapacitor energy storage, are selected and configured to effectively balance the power grid load and improve the stability and economy of the power grid.

[0044] (3) Based on the configuration of the energy storage system, a hierarchical optimization strategy is formulated. These strategies cover multiple levels such as grid structure, energy dispatch, and load management, aiming to minimize grid operating costs and maximize energy utilization efficiency. After the strategy is implemented, a comprehensive evaluation of the grid is conducted, including indicators such as energy efficiency, stability, and economy, and the strategy is iteratively optimized based on the evaluation results.

[0045] (4) It not only improves the operation efficiency and energy utilization efficiency of the power grid, but also significantly enhances the flexibility and reliability of the power grid, providing strong support for the intelligent management of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the solutions in the present invention, a brief introduction is given below to the drawings required for use in describing the embodiments of the present invention. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0047] Figure 1 is a flow chart of an embodiment of the condition monitoring and hierarchical optimization method of the present invention;

[0048] Figure 2 It is a structural diagram of an embodiment of the condition monitoring and hierarchical optimization device of the present invention;

[0049] Figure 3 It is a structural diagram of an embodiment of a computer device of the present invention. DETAILED DESCRIPTION

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meanings as commonly understood by those skilled in the art to which this invention belongs. The terms used in the specification of the application are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The terms "including" and "having" and any variations thereof in the specification and claims of the present invention and the accompanying drawings are intended to cover non-exclusive inclusions. The terms "first" and "second" in the specification and claims of the present invention and the accompanying drawings are used to distinguish different objects, not to describe a specific order.

[0051] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0052] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0053] It should be noted that the state monitoring and hierarchical optimization method provided in the embodiment of the present invention is generally executed by a server / terminal device. Accordingly, the state monitoring and hierarchical optimization apparatus is generally provided in the server / terminal device.

[0054] It should be understood that the number of terminal devices, networks and servers is merely illustrative and any number of terminal devices, networks and servers may be provided as required.

[0055] Example 1

[0056] Please refer to Figure 1 , shows a flow chart of an embodiment of the state monitoring and hierarchical optimization method of the present invention. The state monitoring and hierarchical optimization method, based on the multi-energy flow of the distribution network, includes the following steps:

[0057] Step S1, obtaining real-time data of each node in the power grid.

[0058] In this embodiment, the electronic device (e.g., a server / terminal device) on which the condition monitoring and hierarchical optimization method is running can receive the condition monitoring and hierarchical optimization request via a wired connection or a wireless connection. It should be noted that the wireless connection method may include, but is not limited to, 3G / 4G / 5G connection, WiFi connection, Bluetooth connection, WiMAXX connection, Zigbee connection, UWB (ultra wideband) connection, and other wireless connection methods currently known or to be developed in the future.

[0059] The distribution network refers to the power grid that receives electrical energy from the transmission network or regional power plants and distributes it locally or step by step according to voltage to various users through distribution facilities. It includes overhead lines, cables, distribution transformers and other distribution equipment and ancillary facilities. It is the end of the power system, directly connected to users, and plays the role of distributing electrical energy.

[0060] The multi-energy flow in the distribution network refers to the mutual coupling, conversion and transmission of various types of energy such as electricity, heat, cold and gas in the distribution network. This multi-energy flow characteristic helps to improve the regional energy utilization level and ensure the optimal utilization of various regional energy sources. It is one of the important trends in the development of modern distribution networks.

[0061] In this embodiment, step S1, obtaining real-time data of each node in the power grid further includes the following steps:

[0062] S11, acquiring real-time data measured by sensors placed at each node in the power grid.

[0063] Sensors such as current transformers, voltage transformers, and temperature sensors are deployed at various nodes in the power grid, such as substations, distributed energy access points, and load centers, to measure key parameters such as voltage, current, power factor, and temperature.

[0064] Utilize efficient data transmission protocols, such as RS-485 for wired communication or Wi-Fi and LoRa for wireless communication, to transmit the data collected by the sensors to the data center in real time.

[0065] S12, preprocessing the real-time data.

[0066] Clean real-time data to remove outliers and noise, ensuring data accuracy and reliability. Convert real-time data to a unified format for easier processing and analysis. This improves data quality and provides an accurate data foundation for subsequent analysis.

[0067] Step S2: Process and analyze the real-time data to obtain status information of each node in the power grid.

[0068] Status information of each node in the power grid, including but not limited to voltage, current, power, etc.

[0069] In this embodiment, step S2, processing and analyzing the real-time data to obtain the status information of each node in the power grid further includes the following steps:

[0070] S21, performs data fusion on real-time data to form a complete and consistent view of the power grid status.

[0071] Fusion of real-time data from various sensors and systems creates a complete and consistent view of the grid status. Redundancy and complementarity analysis of real-time data from different sources ensures consistency. Taking into account the spatiotemporal characteristics of real-time data, fusion of time series data and spatially distributed data provides a more comprehensive view of the grid status.

[0072] Time series data records the temporal changes in various grid parameters, such as real-time fluctuations in current and voltage, and can reflect the dynamic operation of the grid. Spatial distribution data, on the other hand, reveals the geographic layout and interrelationships of grid equipment and is crucial for understanding the structural characteristics and potential risks of the grid.

[0073] Through time series analysis, we extract trends and anomalies in key grid parameters. Using spatial analysis, we model the spatial distribution of grid equipment, revealing the spatial relationships and interactions between devices. Based on this, we employ a data fusion algorithm to organically combine time series features with spatial distribution information to form a comprehensive view of the grid's status.

[0074] The application of this fused data can more comprehensively reflect the actual operating status of the power grid, not only helping to promptly detect potential faults and safety hazards, but also providing a scientific basis for optimized grid scheduling and emergency response. By continuously monitoring and analyzing fused data, power companies can continuously improve the stability and security of the grid and ensure the reliability and efficiency of power supply.

[0075] S22, comparing the real-time data after data fusion with a preset normal state threshold, obtaining the state information of each node in the power grid, and determining whether the state of each node in the power grid is normal.

[0076] The normal state threshold is set based on the operating specifications of power grid equipment, historical data analysis and safety standards to ensure that it can accurately reflect the normal operating range of each node in the power grid.

[0077] By comparing real-time data with thresholds, the system can quickly determine the status of each node in the power grid, including whether key parameters such as node voltage, current, and power factor are within normal ranges. If data exceeds preset thresholds, the system immediately issues an alarm, prompting operations and maintenance personnel to pay attention and take appropriate measures.

[0078] This process not only improves the automation level of grid monitoring but also significantly enhances the accuracy and timeliness of fault warnings. Based on the real-time status information provided by the system, operations and maintenance personnel can quickly locate potential problems and implement preventive maintenance measures, effectively avoiding grid failures and ensuring the stability and security of power supply.

[0079] Step S3: Analyze the load characteristics of each node in the power grid based on the status information.

[0080] In this embodiment, step S3, analyzing the load characteristics of each node in the power grid according to the status information, further includes the following steps:

[0081] S31, classifying the loads into different types according to their power consumption characteristics.

[0082] According to the power consumption characteristics of the load, such as power consumption time, power consumption, power factor, etc., the load is divided into different types, such as residential load, commercial load, industrial load, etc.

[0083] S32, using historical load data, analyzes load variation patterns and trends.

[0084] Utilize historical load data to extract key characteristic parameters of various load types, such as peak and valley periods, load rate, and volatility. Establish a forecasting model: Select forecasting models such as time series models and machine learning models to forecast future loads and analyze load variation patterns and trends.

[0085] By adopting step S32, reliable load forecasting results are provided for power grid dispatching and operation, which helps to optimize resource allocation.

[0086] Step S4: selecting and configuring an energy storage system based on the results of analyzing the load characteristics of each node in the power grid.

[0087] In this embodiment, step S4, selecting and configuring the energy storage system based on the results of analyzing the load characteristics of each node in the power grid, further includes the following steps:

[0088] S41, analyzing the capacity, power, and response time parameters of the energy storage system based on the load characteristics and demands of the power grid.

[0089] By analyzing historical electricity usage data, we can identify peak and valley loads and calculate the maximum power and stored energy required by the energy storage system. This helps reduce peak loads, lower electricity costs, and ensure stable grid operation during peak hours.

[0090] The power settings of energy storage systems must meet the dynamic demands of the power grid. During peak load periods, the energy storage system should be able to rapidly increase discharge power to alleviate pressure on the grid; during low load periods, the charging power should be reduced to avoid unnecessary impact on the grid. This requires the energy storage system to have rapid response and flexible adjustment capabilities.

[0091] The response time parameter of an energy storage system is crucial. It directly affects its ability to provide timely power support when the grid experiences fluctuations or failures. Therefore, the response time of the energy storage system must be appropriately set based on the grid's stability and reliability requirements to ensure it can respond quickly and effectively at critical moments.

[0092] S42, determining the installation location and capacity allocation of the energy storage system based on the structure and load distribution of the power grid.

[0093] By analyzing the load curve, we can identify peak and valley load periods, as well as the magnitude of load fluctuations. This helps determine the maximum power demand and capacity scale that the energy storage system needs to handle.

[0094] Within the grid structure, attention must be paid to the layout of the transmission and distribution networks, particularly the location and capacity of substations, and the connection methods of power lines. Energy storage systems should be installed as close to load centers or key transmission nodes as possible to minimize power losses and improve response speed.

[0095] Capacity allocation should be determined based on regional load demand, power transmission bottlenecks, and future development plans. For example, areas with high demand during peak load periods should be equipped with larger energy storage systems to provide sufficient power support. The technical performance and economic feasibility of the energy storage system should also be considered to select the most cost-effective energy storage solution.

[0096] Step S5: Formulate a hierarchical optimization strategy based on the energy storage system.

[0097] In this embodiment, step S5, formulating a hierarchical optimization strategy based on the energy storage system further includes the following steps:

[0098] S51, based on the energy storage system, the distribution network is divided into a bottom-level distribution network, a middle-level distribution network, and a top-level distribution network.

[0099] Specific equipment in the power grid, such as transformers, lines, and energy storage devices, can be set as the bottom-level distribution network. The regional power grid network can be set as the middle-level distribution network. The coordinated optimization network of multiple energy sources such as electricity, heat, and natural gas can be set as the top-level distribution network.

[0100] S52, respectively formulate optimization strategies for the bottom-level distribution network, the middle-level distribution network, and the top-level distribution network.

[0101] For the underlying distribution network, device-level optimization can be performed. For example, for transformers, lines, and energy storage devices, optimized operation strategies can be developed, such as adjusting transformer taps and controlling line power flows. Distributed energy optimization can also be performed for the underlying distribution network, optimizing the generation planning and output control of distributed energy resources (such as solar and wind power), improving energy utilization and grid stability.

[0102] For mid-level distribution networks, regional grid optimization can be performed. At the regional grid level, by coordinating the loads and distributed energy resources at each node, supply and demand balance and energy efficiency improvements are achieved. Energy storage system scheduling can also be performed for mid-level distribution networks. Based on the needs of the regional grid and the status of the energy storage system, a scheduling strategy for the energy storage system is developed to achieve optimal utilization of the energy storage system.

[0103] For top-level distribution networks, multi-energy flow collaborative optimization can be performed. At the top level of the grid, this involves the collaborative optimization of multiple energy sources, such as electricity, heat, and natural gas, to achieve complementarity and coordination among these flows. Market mechanisms and price guidance can also be implemented within the top-level distribution network. Combining these market mechanisms and price signals can guide users toward rational electricity consumption and optimal allocation of distributed energy resources, ultimately achieving economic efficiency and sustainability for the grid.

[0104] Step S6: Evaluate and optimize the power grid after implementing the hierarchical optimization strategy.

[0105] In this embodiment, step S6, evaluating and optimizing the power grid after implementing the hierarchical optimization strategy, further includes the following steps:

[0106] S61, select evaluation indicators based on the characteristics and needs of the power grid.

[0107] Given the highly automated, real-time monitoring, and two-way interactive nature of smart grids, energy efficiency indicators can be selected. These include energy conversion efficiency and line loss rate to measure the grid's energy utilization performance. Furthermore, considering the smart grid's ability to accommodate clean energy, the proportion of renewable energy access can be included in the assessment to encourage green, low-carbon electricity production.

[0108] Stability indicators are also an option. Smart grids must be self-healing and able to quickly restore power when faults occur. Therefore, attention should be paid to indicators such as voltage stability, frequency stability, and the grid's self-healing capabilities to ensure stable operation in the face of various challenges.

[0109] Economic indicators are also an option. These include grid construction investment, operating costs, and user electricity costs. By evaluating these indicators, grid resource allocation can be optimized and economic efficiency improved.

[0110] S62, collecting power grid data after implementing the hierarchical optimization strategy, and performing preprocessing and analysis.

[0111] Grid data comes from a variety of sources, including but not limited to smart meters, sensors, traditional power equipment, and external data sources such as meteorological data. This data should be collected in real time, covering key parameters such as voltage, current, frequency, and power, while also recording information such as timestamps and user IDs to provide a comprehensive foundation for subsequent analysis. Leveraging the Internet of Things and big data platforms can improve the efficiency and accuracy of data collection.

[0112] Data preprocessing is a critical step in ensuring data quality. Collected raw data often contains noise, missing values, and outliers, necessitating data cleaning. This includes removing duplicate data, filling missing values (using techniques such as mean imputation and interpolation), addressing outliers (detecting and addressing them using statistical methods or machine learning models), and standardizing and normalizing the data to facilitate subsequent data modeling and analysis. Furthermore, attention should be paid to standardizing data formats to reduce the complexity of data conversion.

[0113] Data analysis is a core step. Preprocessed power grid data is analyzed in depth by building mathematical or machine learning models. Data analysis methods include time series analysis, regression analysis, cluster analysis, and classification analysis. These methods help predict future power demand, identify the causes of power outages, and optimize the allocation of power resources. Data visualization presents analysis results intuitively in charts and graphs, facilitating understanding and decision-making. Professional data visualization tools such as FineBI can create various chart types, supporting multi-dimensional and multi-level data display and data interaction.

[0114] S63, formulate and implement optimization measures based on the preprocessed and analyzed power grid data.

[0115] Analyze preprocessed data. This involves using big data processing frameworks (such as Apache Hadoop and Spark) to monitor and analyze massive amounts of data in real time, identifying potential problems and anomalies in power grid operations. Through methods such as association analysis and cluster analysis, potential connections and patterns between data are discovered, providing a basis for optimization measures.

[0116] Based on the data analysis results, targeted optimization measures can be formulated. For example, if a region's grid load is consistently high, consideration can be given to increasing power supply to that area or optimizing power resource allocation to reduce overload risks. If data analysis reveals frequent grid failures, equipment maintenance and repairs will be strengthened to improve the reliability and stability of grid equipment.

[0117] Through regular data analysis and reporting, we continuously optimize power supply, resource allocation, and other aspects, improving the overall efficiency and stability of the power grid industry. At the same time, we strengthen team collaboration and data sharing to improve the efficiency and effectiveness of optimization measures.

[0118] The implementation of this embodiment has the following beneficial effects:

[0119] (1) Using sensors to obtain key data such as voltage, current, and power at each node in the power grid in real time, and using data processing and analysis algorithms to deeply mine these real-time data to accurately obtain the operating status information of each node in the power grid;

[0120] (2) Based on the acquired status information, further analyze the load characteristics of each node in the power grid, including load volatility, peak and valley periods, and load types, to provide a scientific basis for the subsequent configuration of energy storage systems. Based on the load characteristic analysis results, targeted energy storage systems, such as battery energy storage and supercapacitor energy storage, are selected and configured to effectively balance the power grid load and improve the stability and economy of the power grid.

[0121] (3) Based on the configuration of the energy storage system, a hierarchical optimization strategy is formulated. These strategies cover multiple levels such as grid structure, energy dispatch, and load management, aiming to minimize grid operating costs and maximize energy utilization efficiency. After the strategy is implemented, a comprehensive evaluation of the grid is conducted, including indicators such as energy efficiency, stability, and economy, and the strategy is iteratively optimized based on the evaluation results.

[0122] (4) It not only improves the operation efficiency and energy utilization efficiency of the power grid, but also significantly enhances the flexibility and reliability of the power grid, providing strong support for the intelligent management of the distribution network.

[0123] The present invention can be used in a wide variety of general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. The present invention can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present invention can also be practiced in distributed computing environments in which tasks are performed by remote processing devices connected via a communications network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0124] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware via computer-readable instructions. The computer-readable instructions can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes in the above-described method embodiments. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0125] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the flowcharts of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0126] Example 2

[0127] Further references Figure 2 , as a response to the above Figure 1 The present invention provides an embodiment of a state monitoring and hierarchical optimization device based on the multi-energy flow of the power distribution network. Figure 1 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0128] like Figure 2 As shown, the state monitoring and hierarchical optimization device 70 of this embodiment includes: an acquisition module 71, a processing module 72, an analysis module 73, a configuration module 74, a formulation module 75 and an optimization module 76, wherein:

[0129] An acquisition module 71 is used to acquire real-time data of each node in the power grid;

[0130] The processing module 72 is used to process the real-time data and obtain the status information of each node in the power grid;

[0131] An analysis module 73 is used to analyze the load characteristics of each node in the power grid based on the status information;

[0132] Configuration module 74, for selecting and configuring an energy storage system based on the results of analyzing the load characteristics of each node in the power grid;

[0133] A formulation module 75 is used to formulate a hierarchical optimization strategy based on the energy storage system;

[0134] The optimization module 76 is used to evaluate and optimize the power grid after the hierarchical optimization strategy is implemented.

[0135] The implementation of this embodiment has the following beneficial effects:

[0136] (1) Using sensors to obtain key data such as voltage, current, and power at each node in the power grid in real time, and using data processing and analysis algorithms to deeply mine these real-time data to accurately obtain the operating status information of each node in the power grid;

[0137] (2) Based on the acquired status information, further analyze the load characteristics of each node in the power grid, including load volatility, peak and valley periods, and load types, to provide a scientific basis for the subsequent configuration of energy storage systems. Based on the load characteristic analysis results, targeted energy storage systems, such as battery energy storage and supercapacitor energy storage, are selected and configured to effectively balance the power grid load and improve the stability and economy of the power grid.

[0138] (3) Based on the configuration of the energy storage system, a hierarchical optimization strategy is formulated. These strategies cover multiple levels such as grid structure, energy dispatch, and load management, aiming to minimize grid operating costs and maximize energy utilization efficiency. After the strategy is implemented, a comprehensive evaluation of the grid is conducted, including indicators such as energy efficiency, stability, and economy, and the strategy is iteratively optimized based on the evaluation results.

[0139] (4) It not only improves the operation efficiency and energy utilization efficiency of the power grid, but also significantly enhances the flexibility and reliability of the power grid, providing strong support for the intelligent management of the distribution network.

[0140] Example 3

[0141] To solve the above technical problems, the embodiment of the present invention also provides a computer device. Figure 3 , Figure 3 This is a basic structural block diagram of the computer device in this embodiment.

[0142] The computer device 8 includes a memory 81, a processor 82, and a network interface 83 that are interconnected through a system bus. It should be noted that the figure only shows a computer device 8 having components of a memory 81, a processor 82, and a network interface 83, but it should be understood that it is not required to implement all the components shown, and more or fewer components may be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to a microprocessor, an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), an embedded device, etc.

[0143] The computer device may be a desktop computer, notebook computer, PDA, cloud server, etc. The computer device may interact with the user via a keyboard, mouse, remote control, touchpad, or voice control device.

[0144] The memory 81 includes at least one type of readable storage medium, including flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic storage, a magnetic disk, an optical disk, etc. In some embodiments, the memory 81 may be an internal storage unit of the computer device 8, such as the hard disk or memory of the computer device 8. In other embodiments, the memory 81 may also be an external storage device of the computer device 8, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device 8. Of course, the memory 81 may also include both the internal storage unit of the computer device 8 and its external storage device. In this embodiment, the memory 81 is generally used to store the operating system and various application software installed on the computer device 8, such as computer-readable instructions for the condition monitoring and hierarchical optimization method. In addition, the memory 81 can also be used to temporarily store various types of data that have been output or are about to be output.

[0145] In some embodiments, the processor 82 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor 82 is generally used to control the overall operation of the computer device 8. In this embodiment, the processor 82 is used to execute computer-readable instructions stored in the memory 81 or process data, such as computer-readable instructions for executing the condition monitoring and hierarchical optimization method.

[0146] The network interface 83 may include a wireless network interface or a wired network interface. The network interface 83 is generally used to establish a communication connection between the computer device 8 and other electronic devices.

[0147] The implementation of this embodiment has the following beneficial effects:

[0148] (1) Using sensors to obtain key data such as voltage, current, and power at each node in the power grid in real time, and using data processing and analysis algorithms to deeply mine these real-time data to accurately obtain the operating status information of each node in the power grid;

[0149] (2) Based on the acquired status information, further analyze the load characteristics of each node in the power grid, including load volatility, peak and valley periods, and load types, to provide a scientific basis for the subsequent configuration of energy storage systems. Based on the load characteristic analysis results, targeted energy storage systems, such as battery energy storage and supercapacitor energy storage, are selected and configured to effectively balance the power grid load and improve the stability and economy of the power grid.

[0150] (3) Based on the configuration of the energy storage system, a hierarchical optimization strategy is formulated. These strategies cover multiple levels such as grid structure, energy dispatch, and load management, aiming to minimize grid operating costs and maximize energy utilization efficiency. After the strategy is implemented, a comprehensive evaluation of the grid is conducted, including indicators such as energy efficiency, stability, and economy, and the strategy is iteratively optimized based on the evaluation results.

[0151] (4) It not only improves the operation efficiency and energy utilization efficiency of the power grid, but also significantly enhances the flexibility and reliability of the power grid, providing strong support for the intelligent management of the distribution network.

[0152] Example 4

[0153] The present invention also provides another embodiment, namely, providing a computer-readable storage medium, which stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the above-mentioned state monitoring and hierarchical optimization method.

[0154] The implementation of this embodiment has the following beneficial effects:

[0155] (1) Using sensors to obtain key data such as voltage, current, and power at each node in the power grid in real time, and using data processing and analysis algorithms to deeply mine these real-time data to accurately obtain the operating status information of each node in the power grid;

[0156] (2) Based on the acquired status information, further analyze the load characteristics of each node in the power grid, including load volatility, peak and valley periods, and load types, to provide a scientific basis for the subsequent configuration of energy storage systems. Based on the load characteristic analysis results, targeted energy storage systems, such as battery energy storage and supercapacitor energy storage, are selected and configured to effectively balance the power grid load and improve the stability and economy of the power grid.

[0157] (3) Based on the configuration of the energy storage system, a hierarchical optimization strategy is formulated. These strategies cover multiple levels such as grid structure, energy dispatch, and load management, aiming to minimize grid operating costs and maximize energy utilization efficiency. After the strategy is implemented, a comprehensive evaluation of the grid is conducted, including indicators such as energy efficiency, stability, and economy, and the strategy is iteratively optimized based on the evaluation results.

[0158] (4) It not only improves the operation efficiency and energy utilization efficiency of the power grid, but also significantly enhances the flexibility and reliability of the power grid, providing strong support for the intelligent management of the distribution network.

[0159] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better embodiment. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the various embodiment methods of the present invention.

[0160] Obviously, the embodiments described above are only some embodiments of the present invention, rather than all embodiments. The preferred embodiments of the present invention are given in the accompanying drawings, but they do not limit the patent scope of the present invention. The present invention can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present invention more thorough and comprehensive. Although the present invention has been described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions described in the aforementioned specific embodiments, or to make equivalent replacements for some of the technical features therein. Any equivalent structure made using the contents of the present invention specification and drawings, directly or indirectly used in other related technical fields, is also within the scope of patent protection of the present invention.

Claims

1. A state monitoring and hierarchical optimization method based on multi-energy flow in distribution network, characterized by: The steps include: Obtain real-time data of each node in the power grid; Processing and analyzing the real-time data to obtain status information of each node in the power grid; Analyzing the load characteristics of each node in the power grid based on the status information; Select and configure energy storage systems based on the analysis of load characteristics of each node in the power grid; Formulate a hierarchical optimization strategy based on the energy storage system; Evaluate and optimize the power grid after implementing the hierarchical optimization strategy.

2. The condition monitoring and hierarchical optimization method according to claim 1, characterized in that: The step of obtaining real-time data of each node in the power grid specifically includes: Acquiring the real-time data measured by sensors placed at each node in the power grid; The real-time data is preprocessed.

3. The condition monitoring and hierarchical optimization method according to claim 1, characterized in that: The step of processing and analyzing the real-time data to obtain status information of each node in the power grid specifically includes: Performing data fusion on the real-time data to form a complete and consistent view of the power grid status; The real-time data after data fusion is compared with a preset normal state threshold value to obtain the state information of each node in the power grid and determine whether the state of each node in the power grid is normal.

4. The condition monitoring and hierarchical optimization method according to claim 1, characterized in that: The step of analyzing the load characteristics of each node in the power grid according to the state information specifically includes: According to the power consumption characteristics of the load, the load is divided into different types; Use historical load data to analyze load change patterns and trends.

5. The condition monitoring and hierarchical optimization method according to claim 1, characterized in that: The steps of selecting and configuring the energy storage system based on the results of analyzing the load characteristics of each node in the power grid specifically include: Analyze the capacity, power, and response time parameters of the energy storage system based on the load characteristics and needs of the power grid; Determine the installation location and capacity allocation of the energy storage system based on the structure and load distribution of the power grid.

6. The condition monitoring and hierarchical optimization method according to claim 1, characterized in that: The step of formulating a hierarchical optimization strategy according to the energy storage system specifically includes: According to the energy storage system, the power distribution network is divided into a bottom-level power distribution network, a middle-level power distribution network and a top-level power distribution network; Optimization strategies for the bottom-level power distribution network, the middle-level power distribution network, and the top-level power distribution network are formulated respectively.

7. The condition monitoring and hierarchical optimization method according to any one of claims 1 to 6, characterized in that: The steps of evaluating and optimizing the power grid after implementing the hierarchical optimization strategy specifically include: Select evaluation indicators based on the characteristics and needs of the power grid; Collect grid data after implementing the hierarchical optimization strategy, and perform preprocessing and analysis; Formulate and implement optimization measures based on pre-processed and analyzed power grid data.

8. A state monitoring and hierarchical optimization device, characterized in that: include: Acquisition module, used to obtain real-time data of each node in the power grid; A processing module, configured to process the real-time data and obtain status information of each node in the power grid; an analysis module, configured to analyze the load characteristics of each node in the power grid based on the status information; A configuration module is used to select and configure the energy storage system based on the results of analyzing the load characteristics of each node in the power grid; A formulation module, for formulating a hierarchical optimization strategy according to the energy storage system; The optimization module is used to evaluate and optimize the power grid after implementing the hierarchical optimization strategy.

9. A computer device comprising a memory and a processor, characterized in that: The memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the condition monitoring and hierarchical optimization method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the condition monitoring and hierarchical optimization method according to any one of claims 1 to 7.

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