Active distribution network optimization dispatching method and system

Abstract

The present invention discloses an active distribution network optimization scheduling method and system, which relates to the field of scheduling optimization. The method comprises: the node layer receives the response signal of the controlled unit layer, and performs signal analysis of the response signal to obtain the signal analysis result; the node layer sends the signal analysis result to the main layer, and the main layer performs the global state evaluation of the node layer, and generates a timing adjustment signal with the global state evaluation result; the timing adjustment signal is penalized and compensated to generate a penalty compensation result, and the main layer generates a cross-node excitation signal; the excitation signal is fed back to the node layer, and the node layer executes the response of active distribution scheduling. The method solves the technical problems of low stability, poor supply and demand balancing ability, and high cost of the power system in the prior art, and achieves the technical effect of improving the stability, supply and demand balancing ability and cost-effectiveness of the power system.

Description

Technical Field

[0001] The present application relates to the technical field of distribution network, in particular to the field of dispatch optimization, and specifically to an active distribution network optimization dispatch method and system. Background Art

[0002] An active distribution network is a distribution system that can actively control and manage distributed energy. It uses advanced communication and information technology and power electronic equipment to actively manage the distribution network that is connected to distributed energy on a large scale. It can autonomously coordinate and control distributed energy units of different types and sizes, and ensure the safe and economic operation of the power system while actively absorbing renewable energy. When planning the basic framework of the active distribution network, the main considerations are the types of power transmission in various places and the types of power that need to be transmitted. The results of distribution network planning directly affect the investment and income of the distribution network and the safety, economy and stability of the future operation of the distribution network. The distribution network adopts active management and active planning. The active distribution network provides a new solution to solve the problem of voltage increase and improve the asset utilization rate of the distribution network. In the existing technology, the stability of the active distribution network is poor, and there is often an imbalance between the demand and supply of electricity, resulting in power waste and increased power supply costs.

[0003] In summary, the existing technology has technical problems such as low power system stability, poor supply and demand balancing ability, and high cost. Summary of the invention

[0004] Based on this, it is necessary to provide an active distribution network optimization scheduling method and system that can improve the stability, supply and demand balancing ability and cost-effectiveness of the power system in response to the above technical problems.

[0005] In a first aspect, a method for optimizing and dispatching an active distribution network is provided, and the method is applied to an active distribution network optimizing and dispatching system, and the active distribution network optimizing and dispatching system includes a main body layer, a node layer and a controlled unit layer, and the method includes: the node layer receives a response signal of the controlled unit layer, and performs signal analysis of the response signal to obtain a signal analysis result, wherein the controlled unit layer includes a power source main body, a load main body and an energy storage main body, and the signal analysis process includes response signal processing through a prediction unit and a configuration unit; the node layer sends the signal analysis result to the main body layer, and the main body layer performs a global state evaluation of the node layer, and generates a timing adjustment signal with the global state evaluation result, wherein the global state evaluation includes a stable adaptation evaluation, a supply and demand balance evaluation, and a cost state evaluation; the timing adjustment signal is penalized and compensated to generate a penalty compensation result, and the main layer generates a cross-node excitation signal; the excitation signal is fed back to the node layer, and the node layer performs a response of active distribution scheduling.

[0006] In a second aspect, an active distribution network optimization and dispatching system is provided, the system comprising: a signal analysis result acquisition module, the signal analysis result acquisition module is used for receiving the response signal of the controlled unit layer by the node layer, and performing signal analysis of the response signal to obtain the signal analysis result, wherein the controlled unit layer includes a power source body, a load body and an energy storage body, and the signal analysis process includes response signal processing through a prediction unit and a configuration unit; a timing adjustment signal generation module, the timing adjustment signal generation module is used for the node layer to send the signal analysis result to the main layer, the main layer performs a global state evaluation of the node layer, and generates a timing adjustment signal with the global state evaluation result, wherein the global state evaluation includes a stable adaptation evaluation, a supply and demand balance evaluation, and a cost state evaluation; an excitation signal generation module, the excitation signal generation module is used for penalty compensation of the timing adjustment signal, generates a penalty compensation result, and generates a cross-node excitation signal by the main layer; a distribution scheduling response module, the distribution scheduling response module is used to feed back the excitation signal to the node layer, and the node layer performs the response of active distribution scheduling.

[0007] According to a third aspect, a computer device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps according to the first aspect when executing the computer program.

[0008] According to a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps described in the first aspect are implemented.

[0009] The above-mentioned active distribution network optimization and scheduling method and system adopt this method to solve the technical problems of low stability, poor supply and demand balancing ability and high cost of the balanced power system in the prior art, and achieve the technical effect of improving the stability, supply and demand balancing ability and cost-effectiveness of the power system.

[0010] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 A schematic diagram of a process flow of an active power distribution network optimization scheduling method in one embodiment;

[0012] Figure 2 A schematic diagram of a flow chart of timing adjustment signal generation of an active power distribution network optimization scheduling method in one embodiment;

[0013] Figure 3A structural block diagram of an active power distribution network optimization dispatching system in one embodiment;

[0014] Figure 4 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment.

[0015] Explanation of the reference numerals: signal analysis result acquisition module 11 , timing adjustment signal generation module 12 , excitation signal generation module 13 , power distribution scheduling response module 14 . DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0017] like Figure 1 As shown, the present application provides an active distribution network optimization scheduling method, the method is applied to an active distribution network optimization scheduling system, the active distribution network optimization scheduling system includes a main layer, a node layer and a controlled unit layer, the method includes:

[0018] The node layer receives the response signal of the controlled unit layer, and performs signal analysis of the response signal to obtain a signal analysis result, wherein the controlled unit layer includes a power source body, a load body and an energy storage body, and the signal analysis process includes response signal processing through a prediction unit and a configuration unit;

[0019] An active distribution network is a distribution system that can actively control and manage distributed energy. It uses advanced communication and information technology and power electronic equipment to actively manage distribution networks that are connected to distributed energy on a large scale. It can autonomously coordinate and control distributed energy units of different types and sizes, and ensure the safe and economic operation of the power system while actively absorbing renewable energy. Optimization and dispatching refers to coordinating and optimizing the active distribution network through management strategies, and maximizing the efficiency and reliability of the power system while meeting the power demand. The framework of the active distribution network optimization and dispatching system is obtained, including the main layer, node layer and controlled unit layer. The main layer includes the distribution network body, which is It is used to transmit excitation signals to lower-level nodes, receive feedback signals from lower-level nodes, and perform node voltage over-limit verification on the system. The node layer refers to receiving the excitation signals of the main layer and performing local optimization according to the internal area of ​​the node layer; the controlled unit layer refers to the flexible load and energy storage in the region, which affect the optimization configuration of the node main body. When the energy balance or consumption level index cannot be met, the signal is fed back to the node main body; when each main body realizes its own energy balance according to the upper-level excitation signal, it does not require the participation of other main bodies; when the main body itself cannot meet the energy balance, it optimizes the scheduling according to the global optimization goal, conducts energy interaction for cross-regional quality inspection, and realizes the overall coordinated control of the system. Through optimized scheduling and management, active control and management of distributed energy can be achieved, and the efficiency and stability of the power system can be improved.

[0020] In the active distribution network optimization dispatching system, the controlled unit layer includes power source subject, load subject and energy storage subject, and these subjects each play different roles in the power system. The power source subject is responsible for providing electric energy, the load subject consumes electric energy, and the energy storage subject is used to store and release electric energy to balance the supply and demand of the power system. The operating status and performance of these subjects directly affect the stability and efficiency of the power system. When the node layer receives the response signal of the controlled unit layer, it will perform a signal analysis process, wherein the response signal refers to the main signal of the power source subject and the load subject in the controlled unit layer, including real-time data of power source and real-time data of load, etc. The signal analysis process refers to the response signal processing through the prediction unit and the configuration unit. The prediction unit predicts the future state of the power system based on historical data, real-time data and other relevant information, which helps the node layer to understand the changing trend and demand of the power system in advance, so as to formulate corresponding dispatching strategies. The configuration unit optimizes the configuration of power, load and energy storage resources according to the prediction results and the current system status, such as adjusting the output power of the power source subject, optimizing the consumption mode of the load subject and managing the charging and discharging strategy of the energy storage subject. Through the collaborative work of the prediction unit and the configuration unit, the node layer obtains signal analysis results, including predictions of the power system state, suggestions for optimal configuration of power sources and loads, etc., which can help improve the efficiency and stability of the power system. The node layer provides strong support for the active control and management of the power system by receiving the response signal of the controlled unit layer and performing signal analysis. This signal analysis process includes processing the response signals of the prediction unit and the configuration unit to obtain predictions of the future power system state and suggestions for optimal configuration of resources, thereby achieving the effect of improving the efficiency and stability of the power system.

[0021] The node layer sends the signal analysis result to the main layer, and the main layer performs the global state evaluation of the node layer and generates a timing adjustment signal based on the global state evaluation result, wherein the global state evaluation includes a stable adaptation evaluation, a supply and demand balance evaluation, and a cost state evaluation;

[0022] In the active distribution network optimization and dispatching system, after completing the signal analysis, the node layer will send the signal analysis results to the main layer. The main layer is responsible for performing global status evaluation. The purpose of global status evaluation is to understand the stability, supply and demand balance and cost status of the system, so as to generate corresponding timing adjustment signals. Among them, the global status evaluation includes stable adaptation evaluation, supply and demand balance evaluation and cost status evaluation. Stable adaptation evaluation refers to the evaluation of whether the power supply, load, etc. in the active distribution network optimization and dispatching system are stable; supply and demand balance evaluation refers to the analysis of the supply and demand relationship of the current system, that is, the power supply capacity of the power source body and the load body. Whether the demand is balanced, for example, the dispatched supply of power output is 10, and the demand for energy storage equipment is 8. The excess supply will cause waste of resources. Through comprehensive consideration of power load prediction, power output scheduling and energy storage equipment usage strategy, it lays the foundation for the subsequent realization of supply and demand balance; cost status evaluation is to evaluate the operating cost of the active distribution network optimization scheduling system, including the cost of each link such as power production, transmission, and distribution. By evaluating the electricity cost and transmission cost of different power supplies, it can be achieved to reduce operating costs, improve energy utilization efficiency and optimize resource allocation. Suggestions. After completing the global status evaluation, the main layer will generate a timing adjustment signal based on the evaluation results. The timing adjustment signal contains information on how to adjust the output power of the power source body, optimize the consumption mode of the load body, and manage the charging and discharging strategy of the energy storage body. According to the timing adjustment signal, the controlled unit layer is adjusted accordingly to achieve global optimization and coordinated control of the power system. The active distribution network optimization and dispatching system can monitor and analyze the operating status of the system in real time, and generate corresponding timing adjustment signals according to the global status evaluation results to guide the optimized operation of the power system, thereby improving the stability of the power system, the supply and demand balance capability, and reducing costs.

[0023] Establishing a global evaluation network, wherein the global evaluation network is embedded in the main body layer, and the global evaluation network includes a stability evaluation subnetwork, a supply and demand evaluation subnetwork, a cost evaluation subnetwork and a comprehensive processing subnetwork, the output ends of the stability evaluation subnetwork, the supply and demand evaluation subnetwork and the cost evaluation subnetwork are connected to the comprehensive processing subnetwork, and after the main body layer receives the signal analysis result, the signal analysis result is input into the global evaluation network to perform the evaluation;

[0024] The signal analysis results are input into the stability evaluation subnetwork, the supply and demand evaluation subnetwork and the cost evaluation subnetwork one by one, and the comprehensive processing subnetwork receives the output results, establishes node files for each node, and generates global status evaluation results with the node files.

[0025] In the optimization dispatching system of the active distribution network, in order to comprehensively and comprehensively evaluate the operating status of the power system, a global evaluation network is constructed, which is embedded in the main layer and includes a stability evaluation subnetwork, a supply and demand evaluation subnetwork, a cost evaluation subnetwork and a comprehensive processing subnetwork. The stability evaluation subnetwork is responsible for evaluating the stability of the power system, receiving the signal analysis results, and judging whether the system is in a stable operating state by analyzing key parameters such as the voltage, frequency, and power factor of the power grid; the supply and demand evaluation subnetwork is responsible for evaluating the supply and demand balance of the power system, and analyzing the current power demand and supply situation according to the signal analysis results. The system can determine whether there is a gap or surplus between supply and demand. If there is an imbalance between supply and demand, it will propose corresponding adjustment strategies, such as adjusting the power supply, optimizing load distribution, etc.; the cost evaluation subnetwork, which is responsible for evaluating the operating cost of the power system, and calculating the costs of each link according to the signal analysis results, including power generation cost, transmission cost, distribution cost, etc., and generating evaluation results according to the costs; the comprehensive processing subnetwork, which is the core of the global evaluation network, is responsible for receiving and summarizing the output results of the stability evaluation subnetwork, the supply and demand evaluation subnetwork, and the cost evaluation subnetwork, comprehensively analyzing the output results, generating global state evaluation results, and outputting corresponding timing adjustment signals. When the main layer receives the signal analysis results provided by the node layer, it will input these results into each subnetwork of the global evaluation network one by one. Each subnetwork will process and evaluate the signal analysis results according to its own responsibilities, and output the results to the comprehensive processing subnetwork. The comprehensive processing subnetwork will comprehensively analyze these results, establish node archives at each node, record its operating status and historical data, and generate global state evaluation results through the node archives. By establishing the global evaluation network, the main layer can comprehensively and comprehensively evaluate the operating status of the power system, providing strong support for subsequent optimization scheduling and decision-making.

[0026] Configure stability factor, supply and demand factor and cost factor, where the sum of stability factor, supply and demand factor and cost factor is 1, and each factor corresponds to a constraint interval;

[0027] After receiving the output result, the output result of the same node is stored in the corresponding node path, and it is determined whether the output result satisfies the constraint interval;

[0028] If the constraint interval is satisfied, the output result is weighted calculated by the corresponding factor;

[0029] If the constraint interval is not met, the output result is recorded directly;

[0030] The weighted calculation results and the direct record output results are summarized and stored in the corresponding node path to complete the node archive construction.

[0031] In the active distribution network optimization and dispatching system, stability factors, supply and demand factors and cost factors are configured, wherein the stability factor reflects the importance of power system stability in the global state evaluation, and the value range of the stability factor is usually between 0 and 1, and the specific value depends on the system's demand for stability. For example, if the system has very high requirements for stability, the stability factor can be set to a value close to 1; the supply and demand factor reflects the importance of the supply and demand balance of the power system in the global state evaluation, and its value range is also between 0 and 1, and the specific value depends on the system's emphasis on the supply and demand balance; the cost factor reflects the importance of the system's operating cost in the global state evaluation, and its value range is between 0 and 1, and the specific value depends on the system's requirements for cost control, wherein stability factor + supply and demand factor + cost factor = 1, and a constraint interval is set for each factor, and the constraint interval is used to determine whether the factor needs to be calculated according to the weight; if the factor is within the constraint interval, it means that the operating state of the node meets the system requirements. At this time, the system will pass The corresponding factors are weighted to comprehensively consider factors such as stability, supply and demand balance, and cost. The result of the weighted calculation will be used as the final evaluation of the node in the global status evaluation; if any of the stability factor, supply and demand factor, and cost factor is not within the constraint interval, the factor that is not within the constraint interval will be calculated according to its own data, and the remaining factors will continue to be weighted. For example, if the stability factor is not within the constraint interval, the calculation method is stability factor + supply and demand factor x supply and demand factor weight + cost factor x cost factor weight = output result; the system will summarize the weighted calculation results and store the summary results to the corresponding node path. Each node will have a complete node archive, including its historical data and current status in different evaluation aspects. These node archives can be used for subsequent optimization scheduling decisions, fault analysis, and continuous improvement and upgrading of the system.

[0032] like Figure 2 As shown, node segmentation identification is performed based on the summary result to generate node identification, wherein the node identification includes a to-be-compensated node identification, a compensable node identification and a retained node identification;

[0033] Establish scheduling connections between nodes corresponding to the to-be-compensated node identifiers and the compensable node identifiers, and perform scheduling optimization through node archives;

[0034] A timing adjustment signal is generated according to the scheduling optimization result.

[0035] Based on the summary results, node segmentation identification is performed to generate node identification, wherein the node identification includes the node identification to be compensated, the compensable node identification and the maintained node identification. The node identification to be compensated refers to the node that shows obvious performance deficiencies or stability problems in the global state evaluation, and requires direct compensation or adjustment measures, including nodes with unbalanced supply and demand, high costs or poor stability; the compensable node identification refers to the node that currently performs well, but has certain compensation potential, and can improve performance or stability in the future by optimizing scheduling or increasing energy storage, including nodes with relatively balanced supply and demand, low costs and good stability, and additional compensation measures can be taken to make it better; the maintained node identification refers to the node that performs well in the global state evaluation and does not need to take immediate compensation or adjustment measures. They are usually nodes with stable operation, balanced supply and demand and high cost-effectiveness. Once the node identification is generated, the system will then establish a scheduling connection between the nodes corresponding to the node identification to be compensated and the node identification that can be compensated. These connections will ensure that during the optimization scheduling process, these nodes can work together to maximize system performance and stability. By utilizing the historical data and current status information in the node archives, the system executes a scheduling optimization algorithm to find the best scheduling method. The goals of scheduling optimization include maximizing stability, minimizing the gap between supply and demand, or reducing operating costs. Based on the results of scheduling optimization, the system generates corresponding timing adjustment signals. These signals will guide the controlled unit layer in the node layer to make corresponding adjustments to ensure that the system operates according to the optimal scheduling strategy. The timing adjustment signals include adjusting the output power of the power source body, optimizing the consumption mode of the load body, and managing the charging and discharging strategy of the energy storage body. Through the above method, the active distribution network optimization scheduling system can more accurately identify and manage the key nodes in the system, and achieve global optimization and collaborative control of the system through scheduling optimization and the generation of timing adjustment signals.

[0036] Record the interaction data between nodes at the node layer, and generate the penalty factor between nodes based on the interaction data recording results;

[0037] The timing adjustment signal is combined and compensated with the inter-node penalty factor to generate a penalty compensation result.

[0038] When each node in the node layer performs its function, a large amount of interaction data will be generated. These data include power exchange, information transmission, control instructions, etc. between nodes. The system needs to record these interaction data in real time for subsequent analysis and processing; based on the interaction data recording results between nodes, the system can analyze the behavior patterns and mutual influence between nodes. If there are frequent incoordination or conflicts between nodes, the performance of the entire system may be degraded. For this degree of incoordination that leads to system performance degradation, the system can generate an inter-node penalty factor, which is a numerical value used to indicate the degree of incoordination or conflict between nodes. It can be calculated based on multiple aspects of the interaction data, such as interaction frequency, fluctuation of interaction power, response delay of control instructions, etc. The higher the value of the inter-node penalty factor, the higher the degree of incoordination between nodes, and corresponding compensation or adjustment is required. After the inter-node penalty factor is generated, the system can use it to perform combined compensation for the timing adjustment signal. The purpose of combined compensation is to reduce the incoordination between nodes and improve the overall performance of the system. Specifically, the system will weight or adjust the corresponding timing adjustment signal according to the value of each inter-node penalty factor. For example, for node combinations with higher penalty factor values, the system may increase the corresponding adjustment amplitude or priority to ensure their collaborative work. The system can comprehensively consider the interaction between nodes and make more accurate and effective combination compensation for timing adjustment signals. This helps improve the stability, efficiency and reliability of the system.

[0039] Determine whether the nodes in the node layer have a connected relationship;

[0040] If there is a connected relationship, the node retention factor is generated according to the connection status;

[0041] The timing adjustment signal is combined and compensated by using the inter-node penalty factor and the node retention factor, and the penalty compensation result is updated.

[0042] The system needs to determine whether the nodes in the node layer have a connected relationship. By checking the communication link, physical connection or logical dependency between the nodes, if there is a direct or indirect connection path between the two nodes, they are considered to be connected nodes; after determining that there is a connected relationship between the nodes, the system will then generate a node retention factor based on the connection status. The node retention factor is a numerical value used to quantify the importance of maintaining the current connection status between the nodes. The more important the connection relationship between the nodes, the larger the node retention factor. The calculation of the node retention factor can be based on multiple factors, such as connection stability, data transmission efficiency, historical connection duration, etc. For example, if the connection between two nodes is very stable, the data transmission efficiency is high, and they have always maintained a good connection status in history, then their node retention factors will be correspondingly high. After generating the inter-node penalty factor and the node retention factor, the system will use these two factors to perform combined compensation on the timing adjustment signal. The purpose of the combined compensation is to consider reducing the incoordination between nodes and maintaining a stable connection between nodes when adjusting the node behavior. Specifically, the system will weight or adjust the timing adjustment signal accordingly according to the values ​​of the inter-node penalty factor and the node retention factor. For example, for a node combination with a high penalty factor but also a high retention factor, the system may moderately adjust the timing of the node combination to reduce incoordination while ensuring stable connection. For a node combination with a low penalty factor but a high retention factor, the system pays more attention to maintaining their connection status. Through combined compensation, the system can obtain an updated penalty compensation result. This result takes into account the connection status and degree of incoordination between nodes more comprehensively, which helps the system make more reasonable and effective decisions in the process of optimizing scheduling. By judging the node connection relationship, generating the node retention factor, and performing combined compensation on the timing adjustment signal, the system can more accurately adjust the node behavior and improve the stability and efficiency of the system.

[0043] Perform penalty compensation on the timing adjustment signal to generate a penalty compensation result, and generate a cross-node excitation signal by the main layer;

[0044] Penalty compensation means that the two node combinations can take into account the influence of each other during the adjustment and optimization process to avoid unnecessary conflicts or oscillations. Taking into account the penalty factors and node retention factors between nodes, the system will perform penalty compensation on the timing adjustment signal, including adjustments to the signal weight, amplitude, timing, etc., to ensure that the system meets the stability and efficiency requirements while reducing conflicts and oscillations between nodes. After completing the penalty compensation, the system needs to ensure that each node can work together to achieve global optimization. To this end, the main layer will generate cross-node excitation signals. First, the main layer will analyze the global state, including the supply and demand status of the system, stability indicators, and the connection status between nodes, etc., to determine the nodes that need to be stimulated. Then, based on the results of the global state analysis, the main layer will generate cross-node excitation signals. The excitation signal includes power adjustment instructions, optimization scheduling instructions, control strategy adjustments, etc., with the purpose of guiding nodes to work together and improving the overall performance of the system. The excitation signal will be transmitted to the node layer to guide each node to make corresponding adjustments. By performing penalty compensation on the timing adjustment signal and combining the cross-node excitation signal generated by the main layer, the system can better coordinate the behavior of each node during the optimization scheduling process and improve the stability and efficiency of the system.

[0045] The excitation signal is fed back to the node layer, and the node layer executes the response of active power distribution scheduling.

[0046] The main layer generates cross-node excitation signals and transmits the excitation signals to the node layer through the communication channel in a timely manner to ensure that the signals can reach the target node accurately in the shortest time. After receiving the excitation signal, the node layer needs to parse the signal and understand the instructions, such as adjusting the power output, etc. Based on the parsed excitation signal, the node layer will adjust its own behavior, including power output, load control, energy storage management, etc., and actively respond to power distribution scheduling, aiming to optimize the operating status of the node and improve the overall performance and stability of the system. Feedback the excitation signal to the node layer, and the node layer will execute the response of active power distribution scheduling. The system can improve the response speed and flexibility, ensure the collaborative work between the node layer and the main layer, and achieve global optimization and efficient operation.

[0047] Obtaining the main signals of the power source main body and the load main body from the response signal, and sending the main signals to the prediction unit to perform power generation prediction of the power source main body and demand prediction of the load main body;

[0048] The prediction result and the energy storage signal of the energy storage subject are synchronized to the configuration unit, supply and demand adaptation is performed, signal analysis is completed according to the supply and demand adaptation result, and a signal analysis result is generated.

[0049] The system first extracts key information from the response signal received at the node layer, including the real-time operating status and performance data of the power source and load. From the response signal, the system extracts the main signals of the power source and load. The main signals of the power source include power generation, power generation efficiency, fuel consumption, etc.; the main signals of the load include load demand, load change rate, voltage and current and other parameters; the extracted main signals are sent to the prediction unit, which predicts the future power generation of the power source based on the received main signal of the power source, using historical data, weather conditions, electricity price information and other factors. At the same time, the prediction unit also predicts the future demand of the load based on the main signal of the load, combined with historical load data, seasonal changes, user behavior patterns and other factors. The energy storage signal includes key parameters such as the current energy storage level, energy storage efficiency, and charging and discharging capacity of the energy storage device; the prediction unit synchronizes the power generation prediction results and demand prediction results, as well as the energy storage signal to the configuration unit. The configuration unit is responsible for adapting supply and demand and optimizing configuration based on these data. The configuration unit performs supply and demand adaptation analysis based on the prediction results and energy storage signals, and evaluates the regulation capacity and potential of the energy storage device by comparing the predicted power generation with the predicted load demand. After completing the supply and demand adaptation, the configuration unit generates signal analysis results, including optimized scheduling instructions, charging and discharging plans for energy storage devices, supply and demand forecasts for specific time periods, and risk assessments. Through the above steps, the system can perform accurate supply and demand adaptation and optimized scheduling based on real-time data and prediction results to ensure the stable and efficient operation of the active distribution network.

[0050] like Figure 3 As shown, the embodiment of the present application includes an active distribution network optimization scheduling system, the system comprising:

[0051] A signal analysis result obtaining module 11, wherein the signal analysis result obtaining module 11 is used to receive the response signal of the controlled unit layer by the node layer, and perform signal analysis of the response signal to obtain the signal analysis result, wherein the controlled unit layer includes a power source body, a load body and an energy storage body, and the signal analysis process includes response signal processing through a prediction unit and a configuration unit;

[0052] A timing adjustment signal generating module 12, wherein the node layer sends the signal analysis result to the main layer, and the main layer performs a global status evaluation of the node layer and generates a timing adjustment signal based on the global status evaluation result, wherein the global status evaluation includes a stable adaptation evaluation, a supply and demand balance evaluation, and a cost status evaluation;

[0053] An excitation signal generating module 13, the excitation signal generating module 13 is used to perform penalty compensation on the timing adjustment signal, generate a penalty compensation result, and generate an excitation signal across nodes by the main layer;

[0054] The power distribution scheduling response module 14 is used to feed back the excitation signal to the node layer, and the node layer executes the active power distribution scheduling response.

[0055] Furthermore, the embodiment of the present application also includes:

[0056] A global evaluation network establishment module, wherein the global evaluation network establishment module is used to establish a global evaluation network, wherein the global evaluation network is embedded in the main body layer, and the global evaluation network includes a stability evaluation subnetwork, a supply and demand evaluation subnetwork, a cost evaluation subnetwork and a comprehensive processing subnetwork, the output ends of the stability evaluation subnetwork, the supply and demand evaluation subnetwork and the cost evaluation subnetwork are connected to the comprehensive processing subnetwork, and after the main body layer receives the signal analysis result, the signal analysis result is input into the global evaluation network to perform the evaluation;

[0057] A global status evaluation result generation module is used to input the signal analysis results into the stability evaluation subnetwork, the supply and demand evaluation subnetwork and the cost evaluation subnetwork one by one, and the comprehensive processing subnetwork receives the output results, and establishes node files for each node, and generates global status evaluation results with the node files.

[0058] Furthermore, the embodiment of the present application also includes:

[0059] A factor configuration module, wherein the factor configuration module is used to configure a stability factor, a supply and demand factor, and a cost factor, wherein the sum of the stability factor, the supply and demand factor, and the cost factor is 1, and each factor corresponds to a constraint interval;

[0060] An output result judgment module, wherein the output result judgment module is used to store the output result of the same node to the corresponding node path after receiving the output result, and judge whether the output result satisfies the constraint interval;

[0061] A constraint interval satisfaction module, wherein the constraint interval satisfaction module is used to perform weighted calculation of output results through corresponding factors if the constraint interval is satisfied;

[0062] An output result recording module, wherein the output result recording module is used to directly record the output result if the constraint interval is not satisfied;

[0063] The node archive construction completion module is used to summarize the weighted calculation results and the direct record output results, and store the summarized results to the corresponding node path to complete the node archive construction.

[0064] Furthermore, the embodiment of the present application also includes:

[0065] A node identification generation module, the node identification generation module is used to perform node segmentation identification based on the summary result, and generate node identifications, wherein the node identifications include to-be-compensated node identifications, compensable node identifications, and retained node identifications;

[0066] A scheduling optimization module, which is used to establish a scheduling connection between nodes corresponding to the to-be-compensated node identifier and the compensable node identifier, and perform scheduling optimization through the node archive;

[0067] A timing adjustment signal generation module is used to generate a timing adjustment signal according to a scheduling optimization result.

[0068] Furthermore, the embodiment of the present application also includes:

[0069] An inter-node penalty factor generation module, wherein the inter-node penalty factor generation module is used to record the interaction data between nodes at the node layer and generate an inter-node penalty factor based on the interaction data recording result;

[0070] A penalty compensation result generating module is used to perform combination compensation on the timing adjustment signal with the inter-node penalty factor to generate a penalty compensation result.

[0071] Furthermore, the embodiment of the present application also includes:

[0072] A connected relationship determination module, which is used to determine whether nodes in the node layer have a connected relationship;

[0073] A node retention factor generation module, wherein the node retention factor generation module is used to generate a node retention factor according to a connection state if a connected relationship exists;

[0074] A penalty compensation result updating module is used to perform combined compensation on the timing adjustment signal with the inter-node penalty factor and the node retention factor, and update the penalty compensation result.

[0075] Furthermore, the embodiment of the present application also includes:

[0076] A demand forecasting execution module, the demand forecasting execution module is used to obtain the main signals of the power source main body and the load main body from the response signal, and send the main signals to the forecasting unit to perform power generation forecasting of the power source main body and demand forecasting of the load main body;

[0077] A signal analysis result generation module is used to synchronize the prediction result and the energy storage signal of the energy storage subject to the configuration unit, perform supply and demand adaptation, complete signal analysis according to the supply and demand adaptation result, and generate a signal analysis result.

[0078] For a specific embodiment of an active distribution network optimization dispatching system, please refer to the embodiment of an active distribution network optimization dispatching method described above, which will not be repeated here. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0079] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 4 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store news data and data such as time attenuation factors. The network interface of the computer device is used to communicate with an external terminal through a network connection. The computer program is executed by the processor to implement an active distribution network optimization scheduling method.

[0080] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0081] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, steps of an active distribution network optimization scheduling method are implemented.

[0082] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of an active distribution network optimization scheduling method are implemented.

[0083] The technical features of the above embodiments may 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, they should be considered to be within the scope of this specification.

[0084] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. An active distribution network optimization dispatching method, the method is applied to an active distribution network optimization dispatching system, the active distribution network optimization dispatching system includes a main layer, a node layer and a controlled unit layer, characterized in that: The method comprises: The node layer receives the response signal of the controlled unit layer, and performs signal analysis of the response signal to obtain a signal analysis result, wherein the controlled unit layer includes a power source body, a load body and an energy storage body, and the signal analysis process includes response signal processing through a prediction unit and a configuration unit; The node layer sends the signal analysis result to the main layer, and the main layer performs the global state evaluation of the node layer and generates a timing adjustment signal based on the global state evaluation result, wherein the global state evaluation includes a stable adaptation evaluation, a supply and demand balance evaluation, and a cost state evaluation; Perform penalty compensation on the timing adjustment signal to generate a penalty compensation result, and generate a cross-node excitation signal by the main layer; Feeding back the excitation signal to the node layer, and the node layer executing the active power distribution scheduling response; The process of performing the global status evaluation of the node layer by the main layer includes: Establishing a global evaluation network, wherein the global evaluation network is embedded in the main body layer, and the global evaluation network includes a stability evaluation subnetwork, a supply and demand evaluation subnetwork, a cost evaluation subnetwork and a comprehensive processing subnetwork, the output ends of the stability evaluation subnetwork, the supply and demand evaluation subnetwork and the cost evaluation subnetwork are connected to the comprehensive processing subnetwork, and after the main body layer receives the signal analysis result, the signal analysis result is input into the global evaluation network to perform the evaluation; The signal analysis results are input into the stability evaluation subnetwork, the supply and demand evaluation subnetwork and the cost evaluation subnetwork one by one, and the comprehensive processing subnetwork receives the output results, and establishes node files of each node, and generates global status evaluation results with the node files; Wherein, the method comprises: Configure stability factor, supply and demand factor and cost factor, where the sum of stability factor, supply and demand factor and cost factor is 1, and each factor corresponds to a constraint interval; After receiving the output result, the output result of the same node is stored in the corresponding node path, and it is determined whether the output result satisfies the constraint interval; If the constraint interval is satisfied, the output result is weighted calculated by the corresponding factor; If the constraint interval is not met, the output result is recorded directly; Summarize the weighted calculation results and the direct record output results, and store the summary results in the corresponding node path to complete the node archive construction; Wherein, the method comprises: Perform node segmentation identification based on the summary result to generate node identification, wherein the node identification includes a to-be-compensated node identification, a compensable node identification, and a retained node identification; Establish scheduling connections between nodes corresponding to the to-be-compensated node identifiers and the compensable node identifiers, and perform scheduling optimization through node archives; A timing adjustment signal is generated according to the scheduling optimization result.

2. The method according to claim 1, characterized in that The method comprises: Record the interaction data between nodes at the node layer, and generate the penalty factor between nodes based on the interaction data recording results; The timing adjustment signal is combined and compensated with the inter-node penalty factor to generate a penalty compensation result.

3. The method according to claim 2, characterized in that The method comprises: Determine whether the nodes in the node layer have a connected relationship; If there is a connected relationship, the node retention factor is generated according to the connection status; The timing adjustment signal is combined and compensated by using the inter-node penalty factor and the node retention factor, and the penalty compensation result is updated.

4. The method according to claim 1, characterized in that The performing of signal analysis of the response signal to obtain a signal analysis result includes: Obtaining the main signals of the power source main body and the load main body from the response signal, and sending the main signals to the prediction unit to perform power generation prediction of the power source main body and demand prediction of the load main body; The prediction result and the energy storage signal of the energy storage subject are synchronized to the configuration unit, supply and demand adaptation is performed, signal analysis is completed according to the supply and demand adaptation result, and a signal analysis result is generated.

5. An active distribution network optimization dispatching system, characterized in that: For executing the method according to any one of claims 1 to 4, the system comprises: A signal analysis result acquisition module, the signal analysis result acquisition module is used to receive the response signal of the controlled unit layer by the node layer, and perform signal analysis of the response signal to obtain the signal analysis result, wherein the controlled unit layer includes a power source body, a load body and an energy storage body, and the signal analysis process includes response signal processing through a prediction unit and a configuration unit; A timing adjustment signal generation module, wherein the node layer sends the signal analysis result to the main layer, and the main layer performs a global status evaluation of the node layer and generates a timing adjustment signal based on the global status evaluation result, wherein the global status evaluation includes a stable adaptation evaluation, a supply and demand balance evaluation, and a cost status evaluation; An excitation signal generation module, the excitation signal generation module is used to perform penalty compensation on the timing adjustment signal, generate a penalty compensation result, and generate a cross-node excitation signal by the main layer; A power distribution scheduling response module, wherein the power distribution scheduling response module is used to feed back the excitation signal to the node layer, and the node layer executes the active power distribution scheduling response.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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