Active service-based distribution network planning scheme optimization method and system
By collecting data in real time through smart meters and sensor equipment, combined with optimization algorithms and intelligent scheduling, an intelligent distribution network planning system is built, which solves the diverse needs and proactive service challenges of traditional distribution network planning, and realizes dynamic optimization of the distribution network and user-customized services.
Patent Information
- Application Number
- CN202510724066.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-02
- Publication Date
- 2025-09-09
AI Technical Summary
Traditional distribution network planning methods are difficult to meet the diverse needs of modern power grids for safety, reliability and economy. In addition, distribution network planning based on active services faces challenges such as data security and privacy protection, and the adaptability and accuracy of models in complex environments.
Through smart meters and sensor equipment, real-time collection of grid operation data is carried out, and dynamic adjustments are made using optimization algorithms. Combined with intelligent scheduling and real-time monitoring, line layout and equipment configuration are optimized, and planning optimization solutions are integrated with actual systems to build an intelligent and adaptive distribution network planning system.
It has improved the power supply reliability, economy and intelligence level of the distribution network, reduced power outage time, optimized resource allocation and energy storage equipment management, and improved the emergency response capability of the power grid and user satisfaction.
Smart Images
Figure CN120613844A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of distribution network planning, and in particular to a distribution network planning scheme optimization method and system based on active service. Background Art
[0002] With the continued growth of electricity demand and the widespread integration of distributed energy resources, traditional distribution network planning methods are unable to meet the diverse security, reliability, and economical requirements of modern power grids. Integrating proactive service concepts into distribution network planning—dynamically adjusting planning strategies through real-time sensing of load changes, distributed generation output, and user needs—is becoming a key path to improving distribution network planning quality.
[0003] Proactive services emphasize a shift in distribution networks from traditional passive response to proactive perception, analysis, and decision-making. Leveraging advanced sensor technology, communication networks, and data analysis algorithms, they collect real-time data on grid operating status, user electricity usage, and distributed energy generation. Through in-depth mining and analysis of this data, they can proactively identify potential power supply and demand imbalances, equipment failure risks, and other issues, and proactively adjust planning strategies.
[0004] Distribution network planning based on active services still faces some challenges, such as data security and privacy protection, and the adaptability and accuracy of models in complex environments.
[0005] Therefore, those skilled in the art provide a method and system for optimizing distribution network planning schemes based on active services, focusing on a method and system for optimizing distribution network planning schemes based on active services, aiming to build an intelligent and adaptive distribution network planning system to solve the problems raised in the above background technology. Summary of the Invention
[0006] In response to the shortcomings of the existing technology, the present invention provides a distribution network planning optimization method and system based on active services. Through data-driven intelligent decision-making, it realizes the transformation of distribution network planning from static to dynamic and from passive to active, effectively improving the power supply reliability, economy and intelligence level of the distribution network, and solving some challenges that distribution network planning based on active services still faces, such as data security and privacy protection, and the adaptability and accuracy of models in complex environments.
[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: A distribution network planning scheme optimization method based on active service includes the following steps: Step S1. Data acquisition and analysis Smart meters and sensor devices collect real-time grid operation data, including load, voltage, and equipment status information, and analyze this data to provide a basis for subsequent optimization decisions; Step S2. Active service mechanism Through intelligent scheduling and real-time monitoring, the distribution network's operating mode can be proactively adjusted, such as load regulation and energy storage equipment management, to improve the grid's emergency response capabilities. Step S3. Planning optimization algorithm Utilize optimization algorithms to optimize distribution network planning, taking into account grid reliability, economy, and sustainable development factors, and optimize line layout, equipment configuration, and operation mode; Step S4. Perform multi-objective optimization Consider multiple objectives, such as minimizing system losses, improving power supply reliability, and reducing investment costs, to achieve optimal overall benefits; Step S5. System Integration Integrate planning optimization schemes with actual distribution network operation systems, dynamically adjust the system through real-time monitoring and data analysis to ensure the effectiveness and flexibility of the planning schemes.
[0008] Furthermore, the real-time collection of grid operation data through smart meters and sensor devices includes: 1) Smart meters installed in the distribution network can monitor each user's electricity consumption in real time and provide detailed load data; 2) Install sensors at key locations such as substations, distribution lines, and switchgear to collect real-time grid operating parameters such as voltage, current, and frequency; 3) Through automated equipment, the status of the power distribution system can be monitored in real time, and fault information, equipment operating status, and load change data can be collected; 4) Collect relevant meteorological data (such as wind speed, temperature, humidity, etc.) through weather stations to help optimize energy scheduling.
[0009] Furthermore, the data are analyzed to provide a basis for subsequent optimization decisions, including: Clean the collected data and remove unreasonable values to ensure the accuracy of subsequent analysis; Using load monitoring data, the real-time load of the power grid is evaluated, and load forecasting models are used to predict future load trends. Common forecasting methods include time series analysis and machine learning. Monitor voltage and current fluctuations in the power grid, especially during high-load periods, analyze whether there are risks of overvoltage or undervoltage, and propose corresponding solutions; Analyze the topology of the distribution network in real time, identify possible bottlenecks or weak links in the power grid, and ensure the stability of power grid operation.
[0010] Furthermore, the active adjustment of the distribution network operation mode through intelligent scheduling and real-time monitoring includes: The intelligent dispatching system monitors voltage fluctuations in real time and automatically adjusts transformer operation as needed to ensure that the grid voltage is within the appropriate range. Based on real-time monitoring data, the intelligent dispatching system automatically generates an optimized grid dispatch plan and dynamically adjusts it based on load changes, equipment operating status, and external factors to ensure that the grid operates in an optimal state. By monitoring the power generation of renewable energy sources (such as solar and wind power), the intelligent dispatching system flexibly adjusts the use of distributed energy, reducing dependence on traditional energy sources while improving the greenness of the power grid; Through the load forecasting model, the load changes in the future can be predicted in advance and the operation strategy of the distribution network can be adjusted. For example, during periods of high load, the load can be reasonably distributed to avoid overloading of certain equipment.
[0011] Furthermore, the optimization algorithm is used to optimize the planning of the distribution network, optimize the line layout, equipment configuration and operation mode, including: By optimizing line configuration and operating modes, power loss is reduced. Genetic algorithms are used to simulate the process of natural selection and genetic inheritance to optimize distribution network planning. Particle swarm optimization algorithms are used to simulate the collective intelligence behavior of particle swarms to find the optimal solution and optimize the topology and equipment configuration of the power grid. Consider system redundancy, load balancing, and equipment fault tolerance to improve grid reliability; On the premise of meeting reliability requirements, equipment investment and operating costs should be minimized as much as possible, so that the distribution network can be flexibly dispatched according to demand fluctuations, especially in the event of load fluctuations and emergencies, to maintain power supply quality.
[0012] Furthermore, the above-mentioned consideration of multiple objectives, such as minimizing system losses, improving power supply reliability, and reducing investment costs, in order to achieve optimal comprehensive benefits, includes: The non-dominated sorting genetic algorithm uses the non-dominated sorting method to sort the solutions and selects the optimal solution by comparing the congestion degree. It can handle multiple optimization objectives at the same time and make a good compromise between the objectives, as follows: Equipment selection and layout optimization: Through intelligent equipment selection and layout optimization, balance equipment reliability, cost, and power quality. For example, appropriate transformers and switchgear are selected, and the location and capacity of various equipment are rationally configured to achieve the best balance between reliability and economy. Load forecasting and dispatch optimization: Utilizing intelligent load forecasting and dispatch algorithms, the system optimizes the grid's operation under varying load conditions. During peak load periods, the system proactively dispatches the output of energy storage devices and renewable energy sources, reducing the use of traditional energy and achieving a balance between economic efficiency and environmental friendliness. Fault self-healing and recovery strategies: Improve grid reliability through intelligent fault detection and automated recovery mechanisms. These mechanisms balance fault recovery speed and recovery cost through multi-objective optimization, ensuring rapid power restoration in the event of a fault. Optimal access to renewable energy and distributed energy: In distribution network planning, multi-objective optimization methods are used to determine the access method for renewable energy (such as solar energy and wind energy).
[0013] Furthermore, the integrated planning optimization scheme and the actual distribution network operation system are dynamically adjusted through real-time monitoring and data analysis, including: Integrate the optimized planning scheme with the actual distribution network system to ensure the scalability and flexibility of the system during implementation; The intelligent dispatching system is integrated and coordinated with other systems to achieve more accurate dispatching and decision-making. For example, by coordinating dispatch with the electricity market, it can dispatch loads during periods of electricity price fluctuations and reduce operating costs. It also integrates meteorological data to predict the impact of weather changes on renewable energy output and conduct precise dispatching.
[0014] Furthermore, a distribution network planning scheme optimization system based on active services includes a perception layer, a communication layer, a management layer, and an application layer. The perception layer is composed of various sensors, smart meters, and distributed energy monitoring equipment, and is responsible for collecting real-time grid operation status data, user electricity consumption data, distributed energy output data, etc., and transmitting the data to the management layer through a communication network; The communication layer adopts a variety of communication technologies, such as fiber optic communication, wireless communication (4G / 5G, Wi-Fi), and power line carrier communication, to build a reliable data transmission channel, realizing fast and accurate data transmission between the perception layer and the management layer, and between the management layer and the application layer. The communication layer has the characteristics of strong anti-interference ability, large communication bandwidth, and low transmission delay, ensuring the real-time and integrity of data.
[0015] Furthermore, the management layer includes a data management module and a model management module; The data management module is responsible for storing, managing, and maintaining the collected data. It uses distributed database technologies, such as Hadoop Distributed File System (HDFS) and NoSQL databases, to achieve efficient storage and rapid retrieval of massive data, establish data backup and recovery mechanisms, and ensure data security. The model management module uniformly manages the load forecast model, distributed power output forecast model, and distribution network planning optimization model, including model training, updating, evaluation, and deployment. It uses model version management technology to record the training parameters and performance indicator information of different versions of the model to facilitate model optimization and backtracking.
[0016] Furthermore, the application layer includes a planning scheme optimization module, an operation monitoring and early warning module, and a decision support module; The planning scheme optimization module realizes the optimization calculation of the distribution network planning scheme based on the data and models provided by the management. The planning objectives and constraint parameters are input through the human-computer interaction interface. The system automatically generates the optimized distribution network planning scheme and displays the planning results in a visual way, such as the power grid topology diagram, equipment configuration plan, and operation index evaluation. The operation monitoring and early warning module monitors the operation status of the distribution network in real time, compares the actual operation data with the prediction results of the optimization model, and issues early warning information in a timely manner when abnormal conditions are found (such as load overload, voltage limit exceeding, equipment failure, etc.), and notifies the operation and maintenance personnel through SMS, email, and sound and light alarms so that they can take corresponding measures to ensure the safe operation of the power grid; The decision support module provides decision support services for power company managers. Based on big data analysis and optimization model calculation results, it conducts economic and technical comparisons of different planning schemes and evaluates the feasibility and potential risks of the schemes.
[0017] The present invention provides a distribution network planning optimization method and system based on active service, which has the following beneficial effects: The present invention provides a distribution network planning scheme optimization method and system based on active service. Active service can timely discover and solve weak links in power grid operation, reduce the time and scope of power outages, and pay attention to user needs. Through two-way interaction with users, customized power services can be provided to users.
[0018] The present invention provides a distribution network planning scheme optimization method and system based on active services. Active services based on big data analysis can achieve optimal resource allocation, rationally plan the access location and capacity of distributed power sources, reduce line losses, optimize the charging and discharging strategies of energy storage equipment, participate in electricity market arbitrage, and improve the overall economic benefits of the power grid.
[0019] The present invention provides a distribution network planning scheme optimization method and system based on active service. Through data-driven intelligent decision-making, it realizes the transformation of distribution network planning from static to dynamic and from passive to active, effectively improving the power supply reliability, economy and intelligence level of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS
[0020] Figure 1 This is a schematic diagram of the process of the distribution network planning solution optimization method based on active service of the present invention; Figure 2 This is a block diagram of the active service-based distribution network planning solution optimization system of the present invention. DETAILED DESCRIPTION
[0021] The following will be combined with the drawings in the specific embodiments of the present invention to clearly and completely describe the technical solutions in the specific embodiments of the present invention. Obviously, the specific embodiments described are only part of the specific embodiments of the present invention, rather than all the specific embodiments. Based on the specific embodiments of the present invention, all other specific embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention. Example 1
[0022] like Figure 1 As shown, this embodiment provides a distribution network planning solution optimization method based on active service, including the following steps: Step S1. Data acquisition and analysis Smart meters and sensor devices collect real-time grid operation data, including load, voltage, and equipment status information. Specifically: 1) Smart meters installed in the distribution network can monitor each user's electricity consumption in real time and provide detailed load data; 2) Install sensors at key locations such as substations, distribution lines, and switchgear to collect real-time grid operating parameters such as voltage, current, and frequency; 3) Through automated equipment, the status of the power distribution system can be monitored in real time, and fault information, equipment operating status, and load change data can be collected; 4) Collect relevant meteorological data (such as wind speed, temperature, humidity, etc.) through weather stations to help optimize energy scheduling; And analyze these data to provide a basis for subsequent optimization decisions, specifically: Clean the collected data and remove unreasonable values to ensure the accuracy of subsequent analysis; Using load monitoring data, the real-time load of the power grid is evaluated, and load forecasting models are used to predict future load trends. Common forecasting methods include time series analysis and machine learning. Monitor voltage and current fluctuations in the power grid, especially during high-load periods, analyze whether there are risks of overvoltage or undervoltage, and propose corresponding solutions; Analyze the topology of the distribution network in real time, identify possible bottlenecks or weak links in the power grid, and ensure the stability of power grid operation.
[0023] Step S2. Active service mechanism Through intelligent scheduling and real-time monitoring, the distribution network's operating mode can be proactively adjusted, such as load regulation and energy storage equipment management, to improve the grid's emergency response capabilities. The intelligent dispatching system monitors voltage fluctuations in real time and automatically adjusts transformer operation as needed to ensure that the grid voltage is within the appropriate range. Based on real-time monitoring data, the intelligent dispatching system automatically generates an optimized grid dispatch plan and dynamically adjusts it based on load changes, equipment operating status, and external factors to ensure that the grid operates in an optimal state. By monitoring the power generation of renewable energy sources (such as solar and wind power), the intelligent dispatching system flexibly adjusts the use of distributed energy, reducing dependence on traditional energy sources while improving the greenness of the power grid; Through the load forecasting model, the load changes in the future can be predicted in advance and the operation strategy of the distribution network can be adjusted. For example, during periods of high load, the load can be reasonably distributed to avoid overloading of certain equipment.
[0024] Step S3. Planning optimization algorithm Utilize optimization algorithms to optimize distribution network planning, taking into account grid reliability, economy, and sustainable development factors, and optimize line layout, equipment configuration, and operation mode; By optimizing line configuration and operating modes, power loss is reduced. Genetic algorithms are used to simulate the process of natural selection and genetic inheritance to optimize distribution network planning. Particle swarm optimization algorithms are used to simulate the collective intelligence behavior of particle swarms to find the optimal solution and optimize the topology and equipment configuration of the power grid. Consider system redundancy, load balancing, and equipment fault tolerance to improve grid reliability; On the premise of meeting reliability requirements, equipment investment and operating costs should be minimized as much as possible, so that the distribution network can be flexibly dispatched according to demand fluctuations, especially in the event of load fluctuations and emergencies, to maintain power supply quality.
[0025] Step S4. Perform multi-objective optimization Consider multiple objectives, such as minimizing system losses, improving power supply reliability, and reducing investment costs, to achieve optimal overall benefits; The non-dominated sorting genetic algorithm uses the non-dominated sorting method to sort the solutions and selects the optimal solution by comparing the congestion degree. It can handle multiple optimization objectives at the same time and make a good compromise between the objectives, as follows: Equipment selection and layout optimization: Through intelligent equipment selection and layout optimization, balance equipment reliability, cost, and power quality. For example, appropriate transformers and switchgear are selected, and the location and capacity of various equipment are rationally configured to achieve the best balance between reliability and economy. Load forecasting and dispatch optimization: Utilizing intelligent load forecasting and dispatch algorithms, the system optimizes the grid's operation under varying load conditions. During peak load periods, the system proactively dispatches the output of energy storage devices and renewable energy sources, reducing the use of traditional energy and achieving a balance between economic efficiency and environmental friendliness. Fault self-healing and recovery strategies: Improve grid reliability through intelligent fault detection and automated recovery mechanisms. These mechanisms balance fault recovery speed and recovery cost through multi-objective optimization, ensuring rapid power restoration in the event of a fault. Optimal access to renewable energy and distributed energy: In distribution network planning, multi-objective optimization methods are used to determine the access method for renewable energy (such as solar energy and wind energy).
[0026] Step S5. System Integration Integrate planning optimization schemes with actual distribution network operation systems, dynamically adjust the system through real-time monitoring and data analysis to ensure the effectiveness and flexibility of planning schemes; Integrate the optimized planning scheme with the actual distribution network system to ensure the scalability and flexibility of the system during implementation; The intelligent dispatching system is integrated and coordinated with other systems to achieve more accurate dispatching and decision-making. For example, by coordinating dispatch with the electricity market, it can dispatch loads during periods of electricity price fluctuations and reduce operating costs. It also integrates meteorological data to predict the impact of weather changes on renewable energy output and conduct precise dispatching. Example 2
[0027] like Figure 2 As shown, this embodiment provides a distribution network planning scheme optimization system based on active services, including a perception layer, a communication layer, a management layer, and an application layer. The perception layer is composed of various sensors, smart meters, and distributed energy monitoring equipment. It is responsible for collecting real-time grid operation status data, user electricity consumption data, distributed energy output data, etc., and transmits the data to the management layer through a communication network; The communication layer uses a variety of communication technologies, such as optical fiber communication, wireless communication (4G / 5G, Wi-Fi), and power line carrier communication, to build a reliable data transmission channel, enabling fast and accurate data transmission between the perception layer and the management layer, and between the management layer and the application layer. The communication layer has strong anti-interference capabilities, large communication bandwidth, and low transmission latency, ensuring data real-time and integrity. The management layer includes data management module and model management module; The data management module is responsible for storing, managing, and maintaining the collected data. It uses distributed database technologies, such as Hadoop Distributed File System (HDFS) and NoSQL databases, to achieve efficient storage and rapid retrieval of massive data, establish data backup and recovery mechanisms, and ensure data security. The model management module manages the load forecast model, distributed power output forecast model, and distribution network planning optimization model in a unified manner, including model training, updating, evaluation, and deployment. It uses model version management technology to record the training parameters and performance indicator information of different versions of the model to facilitate model optimization and backtracking. The application layer includes a planning scheme optimization module, an operation monitoring and early warning module, and a decision support module; The planning scheme optimization module realizes the optimization calculation of the distribution network planning scheme based on the data and models provided by the management. The planning objectives and constraint parameters are input through the human-computer interaction interface. The system automatically generates the optimized distribution network planning scheme and displays the planning results in a visual way, such as the power grid topology diagram, equipment configuration plan, and operation index evaluation. The operation monitoring and early warning module monitors the operation status of the distribution network in real time, compares the actual operation data with the prediction results of the optimization model, and issues early warning information in a timely manner when abnormal conditions are found (such as load overload, voltage limit exceeding, equipment failure, etc.), and notifies the operation and maintenance personnel through SMS, email, and sound and light alarms so that they can take corresponding measures to ensure the safe operation of the power grid; The decision support module provides decision support services for power company managers. Based on big data analysis and optimization model calculation results, it conducts economic and technical comparisons of different planning schemes and evaluates the feasibility and potential risks of the schemes.
[0028] Taking a regional distribution network as an example, we applied a proactive service-based distribution network planning optimization method and system. By collecting and analyzing historical data, we predicted the load growth trend and distributed energy access scale in the region over the next five years. Using optimization models and algorithms, we developed three distribution network planning schemes: Option 1 is a traditional planning solution that does not consider proactive service factors; Option 2 adds some distributed power access based on traditional planning; Option 3 adopts an optimization planning method based on active services, which comprehensively considers factors such as load forecasting, distributed power output forecasting, and grid operation constraints.
[0029] Simulation evaluations of the three schemes show that: Scheme 3's construction cost is 15% lower than Scheme 1 and 8% lower than Scheme 2; power supply reliability indicators (SAIFI and SAIDI) improve by 20% compared to Scheme 1 and 12% compared to Scheme 2; and distributed energy absorption capacity exceeds 95%, significantly exceeding Schemes 1 and 2. Scheme 3 also offers significant advantages in voltage stability and user satisfaction.
[0030] The above practical cases verify the effectiveness and superiority of the distribution network planning optimization method and system based on active services.
[0031] The present invention focuses on a distribution network planning scheme optimization method and system based on active services, aiming to build an intelligent and adaptive distribution network planning scheme system.
[0032] Although specific embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these specific embodiments without departing from the principles and spirit of the invention, and the scope of the invention is defined by the appended claims and their equivalents.
Claims
1. A distribution network planning optimization method based on active service, characterized in that: The following steps are involved: Step S1. Data acquisition and analysis Smart meters and sensor devices collect real-time grid operation data, including load, voltage, and equipment status information, and analyze this data to provide a basis for subsequent optimization decisions; Step S2. Active service mechanism Through intelligent scheduling and real-time monitoring, the distribution network's operating mode can be proactively adjusted, such as load regulation and energy storage equipment management, to improve the grid's emergency response capabilities. Step S3. Planning optimization algorithm Utilize optimization algorithms to optimize distribution network planning, taking into account grid reliability, economy, and sustainable development factors, and optimize line layout, equipment configuration, and operation mode; Step S4. Perform multi-objective optimization Consider multiple objectives, such as minimizing system losses, improving power supply reliability, and reducing investment costs, to achieve optimal overall benefits; Step S5. System Integration Integrate planning optimization schemes with actual distribution network operation systems, dynamically adjust the system through real-time monitoring and data analysis to ensure the effectiveness and flexibility of the planning schemes.
2. The distribution network planning scheme optimization method based on active service according to claim 1 is characterized in that: The real-time collection of grid operation data through smart meters and sensor devices includes: 1) Smart meters installed in the distribution network can monitor each user's electricity consumption in real time and provide detailed load data; 2) Install sensors at key locations such as substations, distribution lines, and switchgear to collect real-time grid operating parameters such as voltage, current, and frequency; 3) Through automated equipment, the status of the power distribution system can be monitored in real time, and fault information, equipment operating status, and load change data can be collected; 4) Collect relevant meteorological data through weather stations to help optimize energy scheduling.
3. The distribution network planning scheme optimization method based on active service according to claim 1 is characterized in that: The analysis of these data provides a basis for subsequent optimization decisions, including: Clean the collected data and remove unreasonable values to ensure the accuracy of subsequent analysis; Through load monitoring data, the real-time load of the power grid is evaluated, and the load change trend in the future is predicted using the load forecasting model; Monitor voltage and current fluctuations in the power grid, especially during high-load periods, analyze whether there are risks of overvoltage or undervoltage, and propose corresponding solutions; Analyze the topology of the distribution network in real time, identify possible bottlenecks or weak links in the power grid, and ensure the stability of power grid operation.
4. The method for optimizing distribution network planning scheme based on active service according to claim 1, characterized in that: The aforementioned proactive adjustment of the distribution network operation mode through intelligent scheduling and real-time monitoring includes: The intelligent dispatching system monitors voltage fluctuations in real time and automatically adjusts transformer operation as needed to ensure that the grid voltage is within the appropriate range. Based on real-time monitoring data, the intelligent dispatching system automatically generates an optimized grid dispatch plan and dynamically adjusts it based on load changes, equipment operating status, and external factors to ensure that the grid operates in an optimal state. By monitoring the power generation of renewable energy, the intelligent dispatching system flexibly adjusts the use of distributed energy, reducing dependence on traditional energy and improving the greenness of the power grid; Through the load forecasting model, the load changes in the future can be predicted in advance and the operation strategy of the distribution network can be adjusted.
5. The method for optimizing distribution network planning scheme based on active service according to claim 1, characterized in that: The optimization algorithm is used to optimize the planning of the distribution network, optimize the line layout, equipment configuration and operation mode, including: By optimizing line configuration and operating modes, power loss is reduced. Genetic algorithms are used to simulate the process of natural selection and genetic inheritance to optimize distribution network planning. Particle swarm optimization algorithms are used to simulate the collective intelligence behavior of particle swarms to find the optimal solution and optimize the topology and equipment configuration of the power grid. Consider system redundancy, load balancing, and equipment fault tolerance to improve grid reliability; On the premise of meeting reliability requirements, equipment investment and operating costs should be minimized as much as possible, so that the distribution network can be flexibly dispatched according to demand fluctuations, especially in the event of load fluctuations and emergencies, to maintain power supply quality.
6. The method for optimizing distribution network planning scheme based on active service according to claim 1, characterized in that: The above-mentioned considerations include multiple objectives, such as minimizing system losses, improving power supply reliability, and reducing investment costs, to achieve optimal overall benefits, including: The non-dominated sorting genetic algorithm uses the non-dominated sorting method to sort the solutions and selects the optimal solution by comparing the congestion degree. It can handle multiple optimization objectives at the same time and make a good compromise between the objectives, as follows: Equipment selection and layout optimization: Balance equipment reliability, cost, and power quality through intelligent equipment selection and layout optimization; Load forecasting and dispatch optimization: Utilize intelligent load forecasting and dispatch algorithms to optimize the operation of the power grid under different load conditions; Fault self-healing and recovery strategies: Improve grid reliability through intelligent fault detection and automated recovery mechanisms; Optimal access to renewable energy and distributed energy: In distribution network planning, multi-objective optimization methods are used to determine the access method for renewable energy.
7. The method for optimizing distribution network planning scheme based on active service according to claim 1, characterized in that: The integrated planning optimization scheme and the actual distribution network operation system are dynamically adjusted through real-time monitoring and data analysis, including: Integrate the optimized planning scheme with the actual distribution network system to ensure the scalability and flexibility of the system during implementation; The intelligent dispatching system is integrated and coordinated with other systems to achieve more accurate dispatching and decision-making. For example, by coordinating dispatch with the electricity market, it can dispatch loads during periods of electricity price fluctuations and reduce operating costs. It also integrates meteorological data to predict the impact of weather changes on renewable energy output and conduct precise dispatching.
8. A distribution network planning optimization system based on active service, characterized in that: It includes the perception layer, communication layer, management layer and application layer. The perception layer is composed of various sensors, smart meters, and distributed energy monitoring equipment. It is responsible for collecting real-time data on grid operation status, user electricity consumption data, distributed energy output data, etc., and transmits the data to the management layer through the communication network; The communication layer adopts a variety of communication technologies, such as optical fiber communication, wireless communication, and power line carrier communication, to build a reliable data transmission channel to achieve fast and accurate data transmission between the perception layer and the management layer, and between the management layer and the application layer. The communication layer has strong anti-interference ability, large communication bandwidth, and low transmission delay, ensuring the real-time and integrity of the data.
9. The distribution network planning scheme optimization system based on active service according to claim 8 is characterized in that: The management layer includes a data management module and a model management module; The data management module is responsible for storing, managing and maintaining the collected data, using distributed database technology to achieve efficient storage and rapid retrieval of massive data, and establishing a data backup and recovery mechanism to ensure data security; The model management module uniformly manages the load forecast model, distributed power output forecast model, and distribution network planning optimization model, and uses model version management technology to record the training parameters and performance indicator information of different versions of the model to facilitate model optimization and backtracking.
10. The distribution network planning scheme optimization system based on active service according to claim 8, characterized in that: The application layer includes a planning scheme optimization module, an operation monitoring and early warning module, and a decision support module; The planning scheme optimization module realizes the optimization calculation of the distribution network planning scheme based on the data and models provided by the management. The planning objectives and constraint parameters are input through the human-computer interaction interface. The system automatically generates the optimized distribution network planning scheme and displays the planning results in a visual way. The operation monitoring and early warning module monitors the operation status of the distribution network in real time, compares the actual operation data with the prediction results of the optimization model, and issues early warning information in a timely manner when abnormal conditions are found, and notifies the operation and maintenance personnel through SMS, email, and sound and light alarms so that they can take corresponding measures to ensure the safe operation of the power grid; The decision support module provides decision support services for power company managers. Based on big data analysis and optimization model calculation results, it conducts economic and technical comparisons of different planning schemes and evaluates the feasibility and potential risks of the schemes.