Construction of integrated network order system of main distribution network based on power grid operation control platform

Abstract

This study presents a major advance in grid management: the development and deployment of an integrated network command system for the main distribution network. The system integrates cutting-edge information technology, including modules such as command issuance, intelligent routing, security assurance and in-depth data analysis, opening a new era of refined and intelligent power grid management. The research focuses on the application of core technologies such as information communication technology, distributed control system, artificial intelligence and big data analysis, and strengthens the system operation foundation. The chapter on system architecture details the innovative integration of DDQN algorithm and attention mechanism, and carefully constructs intelligent scheduling engine and status monitoring and early warning system, which significantly improves real-time response, decision optimization and active security defense capabilities. Simulation experiments and actual case analysis verify the effectiveness of the system, specifically, the response time is reduced by 75.7%(from 2.1 s to 0.51 s in the traditional system), the data processing speed is still maintained at a high level under high load (100,000 data processing rate is 300/s), and the system stability is as high as 99.97%. The new system also achieved a high degree of automation, reducing annual operation and maintenance costs by 20%, and increasing user satisfaction to 90%, an increase of 28.6% over the previous period. These improvements not only optimize power quality and grid efficiency, but also further confirm that the fault response time is reduced by 30% and the user outage time is reduced by 25%. Therefore, this study not only highlights the innovation of the proposed system, but also demonstrates its significant contribution to accelerating the modernization of power grid management and ensuring safe operation with empirical data.

Introduction

With the acceleration of global energy transformation, smart grid has become an inevitable trend in the development of modern power system. Smart grid uses advanced sensors, communication technology, information technology, automation equipment and control methods to realize real-time monitoring, efficient operation and flexible interaction of power grid (Zou et al. 2018). The development of smart grid puts forward new requirements for grid operation control, mainly including: highly integrated information sharing mechanism to break the phenomenon of “information island”; faster response speed to adapt to dynamic changes in power supply and demand; and higher intelligence level to achieve automatic fault detection, predictive maintenance and optimal resource allocation. The integration of distributed energy systems requires the power grid to be able to monitor the power generation and consumption of each node in real time, and realize the bidirectional flow of energy and microgrid management, which further highlights the importance of the main distribution network integration network command system (Li et al. 2023). The smart grid model is shown in Fig. 1. Figure 1 shows the relationship between electricity markets, services, operations, and electricity production, transmission, distribution, and supply. Market factors such as price and demand affect electricity production and supply, while services depend on a stable supply of electricity. Operations is the key link connecting these elements, coordinating the generation, transmission, distribution and supply of electricity to meet market demand and quality of service requirements. Electricity is produced from a variety of sources, such as wind power facilities, and then transmitted through transmission lines to substations, where it is distributed to consumers. Eventually, electricity is supplied to homes and businesses for use, ensuring they have access to reliable power services.

Fig. 1

Smart grid model

This research aims to design and implement an integrated network command system, which can effectively integrate the main network and distribution network resources, and improve the overall operation efficiency of the power grid through efficient command issuance and execution mechanism. The system will strengthen real-time monitoring of power grid status, optimize dispatching strategies, speed up fault response, and promote effective management and utilization of user-side resources, thus laying a solid foundation for the construction of energy Internet (Zhou et al. 2020).

This study has important significance. First, it promotes the development of power grid towards smarter, more flexible and more efficient direction, and provides solid technical support for energy transformation. Secondly, by improving the fault diagnosis and rapid response capability, this study significantly enhances the reliability and toughness of the power grid, and ensures the safety and stability of power supply. In addition, it improves power quality, meets diversified needs of users, enhances user satisfaction and participation, and further improves user service quality. Finally, this study promotes the consumption of clean energy, reduces carbon emissions, and reduces costs by optimizing power grid operation, achieving double benefits of economy and environment. These results fully demonstrate the far-reaching impact and practical application value of this study (Li et al. 2022a, b, c).

Internationally, smart grid projects in the United States and Europe, such as the Grid Modernization Initiative in the United States and the Smart Grids European Technology Platform in Europe, demonstrate the international cutting-edge grid control and management technology. These projects have accumulated valuable experience in the fields of distributed energy management, advanced metering infrastructure (AMI), wide area monitoring system (WAMS), etc., but also exposed common problems such as lack of standardization and information security challenges. (Huang et al. 2021; He et al. 2018)

In recent years, with the rapid development of smart grid research, many scholars and practitioners have devoted themselves to exploring more efficient power grid operation control strategies. For example, Xu T et al. (Xu et al. 2020) deeply analyzes the intelligent grid information integration platform based on Internet of Things technology, and proposes an enhanced data fusion method, which significantly improves the data processing efficiency and system response speed. Venegas-Zarama JF et al. (Venegas-Zarama et al. 2022) focuses on the security protection system of smart grid, and designs a multi-layer defense model, which effectively resists external attacks and ensures the information security and operation stability of power grid. In addition, Mahzouni-Sani M et al. (Mahzouni-Sani et al. 2019) explores the application of machine learning in power grid fault prediction, and realizes accurate prediction of power grid faults by integrating deep learning algorithms, providing new solutions for rapid response and preventive maintenance.

In this study, we explicitly put forward the following research questions and assumptions: the first problem is how to design and implement an integrated network command system that can effectively integrate the main distribution network resources and significantly improve the operation efficiency of the power grid. We assume that through the deep integration of advanced information and communication technologies and intelligent algorithms, we can break the “information island” and realize the smart grid operation mode of real-time monitoring, efficient dispatching and flexible interaction. In order to verify this hypothesis, this study adopts systematic methodology, including but not limited to: designing a highly integrated network instruction system architecture, which integrates intelligent scheduling engine and state monitoring and early warning system; applying advanced algorithms such as deep reinforcement learning (DDQN) combined with attention mechanism to improve the real-time response ability and decision optimization level of the system; Through simulation experiment and case analysis, the performance of the system is evaluated comprehensively, especially in response time, data processing ability, system stability and user satisfaction.

In summary, this study not only closely tracks the latest research results in the field of smart grids, but also aims to contribute valuable theoretical and practical results to the global energy transition and the development of smart grids by proposing and verifying an innovative integrated network command system solution. Through comparative analysis of related projects and technology development trends at home and abroad, we further clarify the orientation and value of the research, and ensure the cutting-edge and practicality of the research work.

The article demonstrates a breakthrough integrated network command system that combines modern information technology with grid management to revolutionize power system scheduling. The system adopts a modular architecture that includes functions such as command distribution, intelligent routing, security and data analysis, facilitating refined and intelligent grid control. It elucidates a solid foundation for the system through an in-depth study of the application of cutting-edge technologies such as information and communication technology, distributed control, artificial intelligence and big data. The innovative use of algorithms in intelligent dispatch, especially DDQN and attention mechanism, improves the real-time decision optimization capability. Core modules such as intelligent scheduling and monitoring and warning systems strengthen active management to ensure stability and safety. Performance evaluation through simulations and case studies confirms that the system has made significant improvements in response time, data processing, stability and safety, with a positive impact on power quality and efficiency. The system outperforms traditional methods with a higher degree of automation, delivering economic advantages and user satisfaction, affirming its transformative potential in grid management.

This paper discusses systematically from theory to practice on the construction of the integrated network command system of main distribution network. Firstly, the introduction clarifies the background, purpose and significance of the study, as well as the research status at home and abroad. Then, Chap. 2 summarizes the basic concepts and principles of the power grid operation control platform and the integrated network command system of the main distribution network, and demonstrates the necessity of its construction. Chapter 3 discusses system design, including architecture, module partition and key technology analysis. Chapter 4 describes the function realization, including real-time monitoring, intelligent scheduling, fault handling and user interaction mechanism. Chapter 5 discusses system implementation and performance evaluation, evaluates its performance in response time, load balance and user satisfaction, and puts forward optimization measures and enlightenment to provide strategies for long-term development of the system.

Related research

Basic concept of power grid operation control platform

Power grid operation control platform is the core component of modern smart grid. It is a comprehensive system integrating data acquisition, processing, analysis, decision-making and execution. Through high-precision sensor networks, high-speed communication facilities, advanced data analysis algorithms and automated control technology, the platform realizes real-time monitoring of power grid status, optimization of resource allocation, scheduling of power generation and load, and rapid response to abnormal events. Its core role is to improve the operational efficiency and reliability of the power grid, ensure the safety and stability of power supply, and provide support for achieving the goals of efficient use of energy and environmental sustainability (Xu et al. 2020; Hafez et al. 2020).

As the nerve center of smart grid, grid operation control platform undertakes the important responsibilities of monitoring grid status, dispatching resources, optimizing operation and ensuring safety. Its core functions cover data acquisition and processing, state estimation, optimal power flow calculation, safety analysis, dispatching instruction generation and execution, etc., aiming at realizing intelligent management of power grid through highly integrated information technology.

Basic concept: Power grid operation control platform is usually based on hierarchical distributed architecture design, divided into monitoring control layer, network analysis layer and decision support layer. Monitoring control layer is responsible for real-time data collection and preliminary processing, network analysis layer is to collect data in-depth analysis, including state estimation, fault detection, and so on, and decision support layer is based on the analysis results to generate scheduling instructions to guide the actual operation of the power grid.

SmartGridCity Project: Located in Boulder, Colorado, USA, SmartGridCity Project is a typical smart grid implementation case. The project utilizes advanced sensors, communication technology and data analysis capabilities to achieve fine monitoring and dynamic response to the grid. In particular, it integrates smart meters, distributed energy management systems and advanced distribution automation systems, demonstrating how to effectively manage the balance of supply and demand in the grid, improve energy efficiency and facilitate access to renewable energy through a unified operational control platform. The project uses a smart grid solution from Cisco, which leverages Internet of Things (IoT) technology to greatly enhance the flexibility and responsiveness of the grid.

SG-CIM model of State Grid Corporation of China: As one of the largest utilities in the world, State Grid Corporation of China has adopted SG-CIM (Smart Grid Common Information Model) model in its smart grid construction. As a standardized information exchange framework, the model provides a unified standard for data sharing among different systems and devices, which helps to break down “information islands”. Through SG-CIM, the State Grid can realize cross-regional and cross-level data integration and analysis, improving the operational efficiency and decision-making quality of the power grid. The implementation of this model, combined with modern information technologies such as big data analysis and cloud computing, demonstrates the application potential of industrial-grade platforms in complex grid environments.

GE’s Grid Solutions: GE’s Grid Solutions offers a comprehensive smart grid solution that integrates advanced monitoring software, protection and control equipment, and grid asset management tools. Through its Distributed Energy Management System (DERMS), GE helps grid operators effectively manage distributed energy resources, including solar, wind, and energy storage systems, ensuring stable grid operation and efficient resource allocation. The platform demonstrates how industry is applying the latest technologies to meet the challenges of grid modernization, particularly in integrating renewable energy and increasing system flexibility.

Basic principle of command system for integrated network of main distribution network

Based on the concept of smart grid, the integrated network command system of main distribution network closely integrates the traditional independent main network (high voltage transmission network) and distribution network (low voltage distribution network) through highly integrated communication and control technology to form a unified command and coordination operation platform. The system adopts advanced information physical system (CPS) architecture to ensure real-time information interaction and resource sharing between the main network and the distribution network (Venegas-Zarama et al. 2022). Specifically, the system realizes the rapid and accurate issuance and execution of instructions through the following aspects: (1) Integrated information platform: Establish a unified data center, collect and integrate real-time operation data of the main distribution network, break information islands, and realize comprehensive sharing and in-depth analysis of data. (2) Intelligent dispatching algorithm: Artificial intelligence and machine learning technology are applied to automatically generate optimal generation and load dispatching instructions according to the current operating state of the power grid and the predicted load changes. (3) Two-way communication network: Build a high-speed and reliable communication network to ensure that control instructions can be quickly transmitted from the main control center to the distribution network terminal equipment, and collect feedback information to adjust the control strategy. (4) Flexible control strategy: Design control plans for different scenarios, such as fault isolation and recovery, distributed energy access and scheduling, to ensure that the system can respond quickly to changes in various operating conditions.

The system data flow is shown in Fig. 2.

Fig. 2

Data flow

As the core of the integrated network command system of main distribution network, the integrated information platform provides a solid information foundation for power network management through highly integrated data collection and processing mechanism. It not only unifies various data sources from the main network and the distribution network, but also ensures the timeliness and accuracy of information through precise data cleaning and integration procedures. It also greatly enhances the decision support ability through in-depth analysis of these data, paving the way for efficient operation of intelligent scheduling algorithms. The algorithm uses AI and machine learning technology to deeply analyze the power grid dynamics and predict the future load trend, so that it can adaptively generate the optimal scheduling scheme to ensure the efficient allocation of power resources.

However, there are challenges and limitations to moving towards this smart grid vision. First of all, effective fusion of massive heterogeneous data needs to overcome the problems of data standard difference, transmission delay and uneven quality, which puts forward extremely high requirements for data fusion technology. Secondly, building a wide and stable two-way communication network, especially the reliability and security of communication under extreme conditions, is the key to ensure the normal operation of the system, but it is also a major technical barrier. Furthermore, the development and optimization of intelligent scheduling algorithms need to ensure the real-time response, high accuracy and robustness of the algorithms while processing large-scale data sets, which is extremely challenging in technical implementation. In addition, high initial investment, long payback cycles, and the need for innovation in policy and regulatory frameworks are factors that cannot be ignored in planning and implementation. Therefore, although the integrated network command system of the main distribution network has far-reaching significance for promoting the development of smart grid, it is particularly important to fully evaluate and deal with the above challenges and formulate comprehensive strategies and plans before actual deployment.

Necessity of construction of command system for integrated network of main distribution network

With the large number of new loads such as distributed energy sources, electric vehicles and energy storage devices, the operating environment of the power grid has become increasingly complex, which puts forward higher requirements for the flexibility, system toughness and new energy consumption capacity of the power grid. Traditional power grid control systems tend to focus on the management of the main grid level, while ignoring the flexibility and interaction of the distribution network. Through accurate load forecasting and distributed resource management, the integrated system of main distribution network can adjust the operation mode of power grid more flexibly to adapt to the rapidly changing load demand and generation supply (Mahzouni-Sani et al. 2019). In the face of natural disasters, man-made destruction and other emergencies, a single level of control system is difficult to quickly restore power supply. The integrated network command system realizes rapid fault location, isolation and recovery through cross-layer collaborative control, and significantly enhances the self-repair capability and overall toughness of the power grid (Zhang et al. 2022). To sum up, the construction of the main distribution network integrated network command system is not only the inherent demand for the development of smart grid, but also the key technical support for realizing the modernization of power grid, ensuring the safety of power supply and promoting energy transformation.

In traditional power system, main network and distribution network often run independently, and information sharing is not smooth, which leads to low dispatching efficiency and slow Incident Response Service. For example, when a fault occurs on the distribution side, due to the lack of an instant information exchange mechanism, it is difficult for the main network dispatching center to quickly and accurately allocate resources, affecting the fault isolation speed and power supply recovery time. In contrast, the establishment of integrated command system of main distribution network is particularly necessary. Through the integrated information platform, the system realizes real-time sharing and in-depth analysis of cross-level data, and significantly improves the accuracy and timeliness of scheduling decisions. Specifically, the integrated system can optimize resource allocation based on the whole network perspective, reduce power loss and improve the overall operation efficiency of the power grid. In addition, it can also effectively cope with a large number of distributed energy access, promote the efficient consumption and utilization of new energy, which is the traditional discrete system incomparable advantages. Therefore, building an integrated command system is not only an inevitable choice for technological upgrading, but also an important way to modernize the power grid and enhance the flexibility and resilience of the energy system.

Research status

The research status of the integrated network command system of power grid operation control platform and main distribution network mainly focuses on the following aspects:

State Grid Corporation of China proposed to construct a new type of power system under the background of double carbon. The integrated dispatching of main distribution network supports the development of full-voltage and flat power grid regulation business of provincial power grid. The research in this respect proposes a comprehensive simulation power grid platform construction scheme based on the integration of main distribution network, aiming at providing accurate and effective data driving for the training simulation system in the direction of main network dispatching, distribution network dispatching and substation operation and maintenance of provincial training center (Yaprakdal et al. 2019). Under the background of establishing new power system with new energy sources as the main body, the construction scheme of main-distribution integrated dispatching control system is proposed to meet the development demand of new power system. By establishing a dispatching control system architecture that adapts to the participation of distributed power sources and controllable loads in a wide area, this system adopts a method combining cloud control and local control, effectively improving the coordination ability between source network loads and storage and the consumption level of clean energy. The Smart Grid Operation and Optimization Laboratory of Zhejiang University has carried out a series of scientific research projects, including research on autonomous and coordinated operation of multi-regional interconnected integrated energy systems, research and demonstration of key technologies for multi-user interaction in industrial parks, and key technologies and demonstrations of active distribution networks. These studies involve the development of algorithms and software for identification and prediction of source/load characteristics, as well as the construction of active distribution network state estimation and its demonstration platform (Xi et al. 2018). It is pointed out that many factors need to be considered comprehensively in the process of designing the architecture and technical scheme of power grid dispatching control system. At present, the construction mode of power grid dispatching control system mainly includes centralized mode, discrete mode and distributed mode. Each of these patterns has advantages and disadvantages, but the distributed pattern is closer to the actual requirements. Research on the integration of distribution network regulation under smart grid mode points out that with the progress of science and technology and the development of urbanization, intelligence has become a popular trend of social development (Mahdad 2019). This research discusses the new development situation of smart grid under the influence of national grid reform and the influence of distribution network regulation integration. The design scheme of integrated operation management graphic platform is studied and put forward. Through common information model and scalable vector graphic file format, the main network graphic and distribution network graphic are connected and imported into the same power network graphic platform, and the functional modules such as integrated graphic management, real-time monitoring and management of operation mode, daily dispatching management, etc. are realized, thus solving the disadvantages of independent main distribution network management in the past (Schwele et al. 2020).

Currently, although smart grid technology has made significant progress, it still faces several technical limitations, including the increased complexity of data processing, the security risks of cross-system communication, and the insufficient adaptability of algorithmic models in complex grid scenarios. Recent studies have shown that the use of edge computing and fog computing technologies can effectively alleviate data processing pressure, reduce latency, and improve the efficiency and security of data processing by performing preprocessing and analysis in locations close to the data source. For example, a study published in IEEE Transactions on Smart Grid demonstrates how edge computing can enhance the localized processing of grid data, thereby reducing the burden on data centers and improving system response speed. In addition, the application of blockchain technology is gradually being explored in the field of grid communication security to ensure the immutability and source traceability of data transmission and effectively defend against external attacks. Regarding the limitations of algorithmic models, the latest advances in deep learning and reinforcement learning provide new ideas for solving high-dimensional, nonlinear problems, such as scheduling algorithms combining multi-intelligent body systems that can respond to dynamic changes in the power grid more flexibly, and improve the adaptability and robustness of scheduling strategies. In summary, the current research is actively breaking through the existing technical bottlenecks, and through the integration of emerging information technology, it is constantly promoting the integrated command system of main and distribution networks to a higher level of intelligence and automation.

Design of command system for integrated main distribution network

Our DDQN performs complex data analysis and pattern recognition, and is able to automatically adjust grid parameters and optimize resource allocation, which incorporates learning and self-optimization capabilities compared to traditional automation, reflecting the core features of AI. Combining big data analysis and machine learning technology, the intelligent automation framework can monitor the grid operation status in real time and warn of potential failures in advance through prediction models, and this data-driven prediction capability is one of the important applications of AI technology. By analyzing historical and real-time data through deep learning and other technologies, the intelligent automation framework can more accurately assess grid security risks and take preventive measures, reflecting AI’s judgment and decision-making advantages in complex situations.

System architecture design

The main distribution network integrated command system is an important part of power system automation, which aims to realize unified dispatching and management of main network and distribution network through highly integrated information technology and control strategy. The system architecture design needs to comprehensively consider the efficiency, security, reliability and scalability of the system. It usually adopts a layered architecture design method and is divided into four layers: presentation layer, business logic layer, data service layer and physical equipment layer. As the interface between users and system, presentation layer is responsible for displaying system status, receiving user instructions and feeding back operation results. Developed with Web technologies such as HTML5, CSS3, JavaScript and front-end frameworks React or Vue.js, it supports cross-platform access to ensure easy operation and user-friendly interface. The business logic layer processes user requests and executes corresponding business rules, including command validation, authority control, task scheduling, etc. This layer typically leverages a microservices architecture, with each service deployed and scaled independently to improve system flexibility and responsiveness. Frameworks such as Spring Boot and Django provide strong support for this layer development. Data service layer is responsible for data storage, management and retrieval, including real-time data (such as power grid operation parameters), historical data and system configuration information. Distributed database (such as MySQL cluster, MongoDB) and message queue (such as RabbitMQ, Kafka) technologies are adopted to ensure data consistency, integrity and high concurrent access capability. The physical equipment layer includes SCADA system, RTU (remote terminal unit), smart meter, switchgear, etc., which is the interface between the system and the actual power grid. Equipment monitoring, data acquisition and command issuance are realized through industrial communication protocols (such as IEC 61,850 and Modbus) to ensure accurate execution of control instructions (Medani et al. 2018; Sharma and Mishra 2022). The specific framework is shown in Fig. 3.

Fig. 3

System architecture

The system architecture design adopts a layered approach, which is subdivided into display layer, business logic layer, data service layer and physical equipment layer to ensure efficient operation, security guarantee, stability, reliability and flexible expansion of the system. The presentation layer is cross-platform compatible with HTML5, CSS3, JavaScript and front-end frameworks such as React/Vue.js, providing an intuitive and user-friendly interaction experience. The business logic layer adopts the microservice architecture, which is supported by frameworks such as Spring Boot and Django to enable independent deployment and expansion of services, greatly enhancing the flexibility and response speed of the system. The data service layer utilizes distributed database technologies (such as MySQL cluster, MongoDB) and message queues (RabbitMQ, Kafka) to ensure data consistency, integrity and high concurrency processing capabilities to meet the needs of large data volume processing. The physical equipment layer realizes equipment monitoring, data acquisition and instruction issuance through SCADA system, RTU, smart meter, etc., relying on IEC 61,850, Modbus and other industrial communication protocols to ensure accurate execution of instructions.

This architecture is designed for excellent scalability and environmental adaptability. The microservices architecture enables systems to be flexibly deployed and scaled on demand across grid environments of varying sizes and complexity, whether complex urban grids handling large-scale data or mini-grids for remote locations, by adding or removing service instances. The distributed design of the data service layer ensures that data processing capacity dynamically adjusts with system load, ensuring efficient operation during peak data traffic periods. In addition, the loose coupling design between the layers makes the system easy to maintain and upgrade, and can quickly integrate into new technologies or cope with the uncertainty of future grid development, showing strong environmental adaptability and long-term development potential. Therefore, the architecture not only meets the comprehensive command requirements of the current smart grid, but also reserves sufficient space for the intelligent evolution of the future grid.

Module division of network command system

In order to ensure the efficient operation of the integrated network command system of the main distribution network, the system design contains several key modules to work together and improve the overall performance. First, the command generation and approval module automatically creates operation commands, and ensures the legality and security of each command through electronic signature and authority verification processes. Secondly, the intelligent routing module automatically selects the optimal path to issue commands according to the network topology and real-time load conditions, so as to reduce transmission delay and improve system response speed. In addition, the security protection module constructs a multi-level network security protection system by integrating firewall, intrusion detection system and encryption transmission technology to prevent illegal intrusion and data tampering and ensure the operation safety of the system. The condition monitoring and fault diagnosis module monitors the operation status of each link of the power grid in real time, and analyzes abnormal data by using machine learning algorithms to quickly locate fault points and provide solutions, thus enhancing the self-healing ability of the system. Finally, the data analysis and optimization module integrates big data processing technology to deeply analyze the power grid operation data, mine potential laws, and provide scientific basis for power grid planning and operation optimization. The integration of these modules ensures efficient, secure and intelligent operation of the integrated network command system for the main distribution network (Li et al. 2024).

In the design of integrated network command system of main distribution network, module division is very important to ensure efficient coordination of system functions. The specific algorithms and techniques are applied as follows: The command generation and approval module uses digital signature and authorization verification mechanisms, combined with rule-based algorithms, to automatically create and ensure the legitimacy and security of instructions. The intelligent routing module uses graph algorithm and optimization theory to dynamically select the optimal path according to network topology and real-time load, reduce instruction transmission delay and speed up response. The security protection module integrates firewall, intrusion detection system and encryption technology to form a multi-level protection system. It uses behavior analysis algorithm to detect anomalies and effectively prevent illegal intrusion and data tampering. The condition monitoring and fault diagnosis module integrates machine learning algorithms, such as support vector machine (SVM) and random forest, for real-time data analysis and rapid fault location. The data analysis and optimization module introduces big data processing technology, such as large-scale data processing and machine learning model training using Apache Spark, to mine the operation rules of the power grid and provide scientific basis for power grid optimization. For potential module integration problems, service-oriented interface design and microservice architecture are adopted to ensure loose coupling and efficient interaction between modules, while container technology (Docker, Kubernetes) is used to manage service deployment, simplify integration complexity, and improve system stability and maintainability.

Key technology analysis

ICT

ICT is not only the core support of the integrated network command system of the main distribution network, but also runs through all aspects of data acquisition, transmission and processing. In order to ensure real-time and efficient transmission of large data volumes, the system adopts optical fiber communication technology, relying on its Gbps high speed and low latency characteristics, and integrates the fifth generation mobile communication (5G) technology to provide theoretical peak download rates of up to 20 Gbps and ultra-low latency of less than 1ms, enhancing real-time interaction between grid monitoring and command control.

At the transmission optimization level, advanced encoding and decoding technologies such as LDPC and Turbo Codes are applied, combined with efficient video and data compression algorithms (H.265/HEVC, JPEG 2000), to greatly reduce data volume while maintaining information integrity and improve bandwidth efficiency. At the same time, by introducing software-defined networking (SDN) and network function virtualization (NFV), the control plane and the data plane are separated, so that the network resource configuration can be flexibly adjusted according to the dynamic requirements of power grid scheduling${\text{Network Flexibility}}=f(SDN,NFV,{\text{Real-Time Demand}})$. where f denotes the network flexibility enhancement function that varies with SDN, NFV introduction and real-time requirements (Khalid 2019; Wang et al. 2022).

Performance evaluation of information and communication technology (ICT) components usually involves metrics such as data throughput, transmission delay, and bit error rate. The introduction of fiber-optic communication and 5G technology has significantly improved data transmission rates and real-time performance, but accordingly, the initial construction and maintenance costs are high, including fiber laying, 5G base station deployment and spectrum licensing costs. The adoption of encoding and decoding technologies and high-efficiency compression algorithms optimizes bandwidth utilization and reduces data transmission costs, but the development of algorithms and upgrading of embedded systems also constitute a certain overhead. The implementation of software-defined networking (SDN) and network function virtualization (NFV) technologies increases network flexibility and resource allocation efficiency, but involves hardware and software modifications, professional training and operation and maintenance management will also incur additional costs. Therefore, when implementing these technologies, a comprehensive cost-benefit analysis should be conducted, the budget should be reasonably planned, and a phased implementation strategy should be adopted to ensure that the return on investment is maximized. At the same time, through government subsidies, industry cooperation and technology R&D innovation, the threshold of key technology applications should be lowered to promote the sustainable development of the smart grid.

Distributed control system (DCS)

DCS is very important for stable operation and efficient management of power grid through its special architecture design. The control logic is distributed in each field controller, following the principle of “centralized management and decentralized control”. The system reliability can be expressed as${\text{System Reliability}}=\prod\limits_{{i=1}}^{n} ( 1 - {P_{{\text{failure}},i}}){\text{ }}$: where is the failure probability of the \(i\) th controller and n is the total number of controllers. Decentralized control mechanism allows the remaining controllers to continue operating when part of the system fails, significantly enhancing overall system reliability. DCS also has remote monitoring and online upgrade capabilities, and remote management and update of controller software can be realized by SSH and OTA technology to improve maintenance efficiency and system flexibility (Jin et al. 2018).

As an example of DCS application, we can refer to the automation management system of a large petroleum refinery. The system adopts advanced DCS architecture, and accurately controls various equipment through controllers distributed on site, realizing automatic monitoring of the whole process from raw material input to product output. The principle of “centralized management and decentralized control” greatly improves the reaction speed and control accuracy, and at the same time, when a local failure occurs, other controllers can still maintain normal operation, ensuring production continuity and system stability. However, DCS maintenance and update face challenges, including: cross-regional equipment coordination and update synchronization is difficult, requiring highly reliable remote communication technology and fault recovery strategy; software and firmware upgrades need to be carefully implemented to avoid the entire system paralysis caused by errors in the upgrade process; At the same time, with the increasingly severe network security threats, protecting DCS from external attacks and ensuring the authenticity and integrity of control instructions have become a major difficulty in maintenance work. To meet these challenges, organizations often take measures such as regular security audits, strict identity authentication mechanisms, and Incident Response Service plans.

Artificial intelligence and big data technology

The integration of artificial intelligence (AI) and big data technology brings a new perspective to the intelligent management of power grids. Deep learning (DL) models, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), and reinforcement learning (RL) algorithms, can deeply analyze power grid behavior patterns$\hat {Y}={f_\theta }(X)$. where X is the prediction output, X is the input data (such as historical load, weather, etc.), X is the model function, and the parameters are optimized by training. These models can accurately predict load trends, identify potential faults and early warning, optimize dispatching strategies, and improve power grid operation efficiency. In terms of big data processing, Hadoop, Spark and other platforms and cloud computing resources are used in combination with Apache Kafka and Flink real-time stream processing technologies to achieve efficient analysis and processing of data. Its processing capacity can be expressed as Formula 1. In this formula, data throughput, processing time and parallelism jointly determine the efficiency and scale of data processing, providing strong support for continuous improvement of grid efficiency (Ma et al. 2022).

$${\text{Data Processing Capability}}=\frac{{{\text{Data Throughput}}}}{{{\text{Processing Time}}}} \times {\text{Parallelism }}$$

(1)

In the intelligent management of power systems, specific artificial intelligence models, such as long short-term memory networks (LSTM), are widely used in time series forecasting, such as load forecasting. LSTM handles long-term dependency problems through its unique gating mechanism, which accurately captures periodic and trending features in historical load data. During model training, a large number of historical load data and meteorological data are needed as inputs (X), and the model weight parameters are adjusted by back propagation to minimize the prediction error and optimize the model function (X). Data privacy and security are especially important in this process. In order to protect user data, the following measures are usually taken: first, sensitive data is desensitized or differential privacy technology is used to ensure that the original data cannot be recovered; second, encryption algorithms are applied to encrypt the data in transmission to prevent the data from being stolen during transmission; in addition, a secure data access control mechanism is constructed to authorize only specific personnel or systems to access necessary data, following the principle of least privilege. Finally, strict data lifecycle management policies are established to ensure that data can be destroyed in a timely and safe manner when it is no longer needed, thus ensuring the compliance and security of big data processing in the power system.

Construction of integrated network command system of main distribution network

This chapter deepens the realization of intelligent scheduling in the order issuing system of the integrated network of main distribution network, from model construction, parameter configuration to optimization goal, to order execution mechanism and feedback monitoring strategy, exception handling, every step is precise and comprehensive. The attention mechanism, dynamic learning rate adjustment, authority control and observer mode are specially introduced, which highlight the foresight and practicality of the system design, aiming to create a highly intelligent, safe, efficient and adaptable power grid dispatching system (Liu et al. 2020).

Implementation of intelligent schedule algorithm

In the further design and implementation of intelligent scheduling algorithm, we not only adopt the basic deep Q network (QN) to learn the state-action value function, but also embed the double deep learning mechanism (DDQN) to significantly reduce the estimation bias and improve the prediction accuracy. In addition, we innovatively incorporate Attention, which enables the model to focus on the most critical state features, greatly improving the accuracy of decisions. The model framework is shown in Fig. 4.

As shown in Fig. 4, in reinforcement learning, an intelligent body (Agent) first selects an action a based on the current environment state s. Subsequently, a deep neural network policy (DNN policy) predicts the probability distribution or expected reward for the next state based on the state s and action a. The DNN policy predicts the probability distribution or expected reward for the next state based on the state s and action a. Based on these predictions, the intelligent body performs action a and subsequently observes a new state s’ and receives a reward r. By repeating this process over and over again and using trial and error and learning to optimize the policy, the intelligent body aims to achieve the maximum long-term reward.

Fig. 4

Model framework

The model structure becomes more complex and refined, specifically expressed as: Here, and represent the weight matrix and bias respectively, are activation functions, such as ReLU or tanh, and the mechanism strengthens the model’s attention to key state information through dynamic weight allocation. The objective function of the double deep Q network (DDQN): the calculation formula of the attention mechanism: In terms of parameter configuration, we not only consider the traditional learning rate, discount factor, etc., but also add a double confidence factor, which is specially used to correct the error in DDQN, and an attention allocation coefficient, which guides the weight allocation of states and actions in the attention mechanism and strengthens the pertinence of learning. Dynamic learning rate adjustment formula: optimizing the setting of objective function, we break through the traditional framework, not only pursue maximizing expected return, but also encourage exploration by adding entropy term (Li et al. 2022a, b, c; Lu et al. 2022). The objective function becomes:

$[L(\theta )=E[r+\gamma {\hbox{max} _{a^{\prime}}}{Q_{{\theta ^ - }}}(s^{\prime},a^{\prime}) - {Q_\theta }(s,a)] - \eta H(\pi (\pi (\theta )$Here, H represents entropy, which measures the uncertainty of the strategy, the strategy distribution, and as the entropy term coefficient, it stimulates the exploration behavior and promotes the exploration of the algorithm in the unknown domain. As for learning strategy, we adopt dynamically adjusted learning rate, which is adaptive to current performance. The formula such as represents the current performance level and is a preset goal, which ensures that the learning rate is optimized according to the changing situation. Finally, the calculation formula for state normalization: We implement adjustments to dynamic statistics to ensure that the model can adapt to environmental changes, whether it is offset displacement or scale changes in the data distribution, and maintain efficient learning (Vahid-Pakdel and Mohammadi-ivatloo 2018; Song et al. 2022).

In the implementation of intelligent scheduling algorithm, we deeply apply the combination of deep Q network (DQN) and double deep learning mechanism (DDQN), which significantly reduces the estimation bias and improves the prediction accuracy. In particular, the attention mechanism is incorporated into the algorithm, which enables the model to focus on the most critical state features and greatly improves the accuracy of decision making. After the algorithm is implemented, the simulation test and actual power grid data verification show that compared with single DQN, the compound algorithm improves the dispatching efficiency by about 20% and reduces the misdispatch rate by nearly 30%, which fully proves the effectiveness of the algorithm. Especially in the face of complex power grid dynamic changes and emergencies, the algorithm can quickly make accurate judgments, adjust power allocation strategies, and ensure stable operation of the power grid. In addition, we also fine-tune the parameters and adjust the dynamic learning rate of the algorithm to further optimize the learning efficiency and generalization ability of the model.

This advanced AI-driven power grid management framework combines Deep Q-Networks with DDQN and an Attention mechanism, reducing estimation errors and boosting predictive accuracy for informed decision-making. It features tailored parameters, including a dual confidence factor and attention allocation for focused learning, and employs an entropy-driven objective function to balance exploitation and exploration. Adaptive learning rates and state normalization techniques ensure the system is responsive to performance and resilient to data fluctuations, offering a highly adaptable, precise decision engine for complex grid operations.

Core module development

This chapter will discuss the core module development of the integrated network command system of main distribution network, including the design and implementation of intelligent dispatching engine and condition monitoring and early warning system, which are the key to ensure the efficient and safe operation of power grid.

The intelligent dispatching engine is the brain of the whole system, which is responsible for receiving real-time power grid state information, making decisions according to advanced dispatching algorithms, and issuing instructions to specific equipment or subsystems to realize the optimal allocation of power grid resources. Its development focuses on the following aspects (Su and Teh 2023), and the overall flow of the model is shown in Fig. 5.

Figure 5 shows the main components of the integrated network command system of the main distribution network and their interactions. Starting at the top, sensors and transformers collect critical data from the power system, which is cleaned and formatted into a real-time grid load status module. In this module, the system monitors the status of the grid in real time to react quickly. Efficient decision logic design modules are then responsible for processing this data to determine the best course of action. To ensure the security of the system, we implement various security measures such as firewalls and encryption technologies to prevent unauthorized access and data leakage. In addition, fault tolerance and continuity assurance mechanisms are in place to ensure continued service even in the event of a failure. In the middle layer, we have condition monitoring and early warning modules. This module is not only responsible for monitoring the health of the grid, but also for issuing timely warnings to alert operators of possible problems. It uses advanced algorithms to detect any anomalies, which helps us spot potential problems early and act quickly. At the bottom, we have an anomaly detection modeling module that uses machine learning algorithms to identify and classify anomalous events. By learning from historical data, we can build an effective model to predict future anomalies. Finally, we improve the performance of the entire system through model optimization and training, making it better able to adapt to changing grid conditions.

Overall, the system is designed to provide a comprehensive solution to ensure safe, stable and efficient operation of the grid. By combining sensor data, real-time monitoring, decision logic, and advanced analytics, we create a powerful tool that helps operators make better decisions and anticipate and resolve problems before they arise.

Figure 5 shows the main components of a smart grid management system and their interactions. First, the sensors collect real-time data, including grid load status and fault conditions. This data is cleaned and formatted to design efficient decision logic. At the same time, security measures are implemented to protect the system from attacks. Transformers play a key role in this process, converting high-voltage AC power to low-voltage AC power to provide safe and usable power to users. In addition, the system has fault tolerance and continuity guarantees to keep running even in the event of a failure. In order to achieve condition monitoring and early warning, the system models anomaly detection and optimizes models to improve prediction accuracy and response speed. This helps to identify potential problems and take preventive action before a failure occurs, ensuring the stability and reliability of the power supply.

Fig. 5

Overall flow of the model

The essence of the online learning framework lies in its ability to process data streams in real-time and instantly adjust model parameters. Let our model parameters be denoted as $\theta$, with new incoming data samples represented by ${x_t}$ (where t denotes the time step), and the corresponding label or feedback as ${y_t}$. The objective of online learning is to optimize model parameters by minimizing the cumulative loss function $L(\theta )$, which is typically defined as the sum of instantaneous losses at each time step:$L(\theta )=\sum\limits_{{t=1}}^{T} l (f({x_t};\theta ),{y_t})$

Here, l represents the loss function, gauging the discrepancy between the model’s prediction $f({x_t};\theta ){\text{ }}$and the actual outcome ${y_t}{\text{ }}$; T symbolizes the current time step. The crux of online learning involves instant updates to parameters $\theta {\text{ }}$ upon receiving new data $({x_t},{y_t})$, utilizing techniques such as gradient descent:

${\theta _{t+1}}={\theta _t} - \alpha {\nabla _\theta }l(f({x_t};{\theta _t}),{y_t})$

In this equation, $\alpha {\text{ }}$ denotes the learning rate, dictating the magnitude of parameter updates;${\nabla _\theta }l{\text{ }}$ signifies the gradient of the loss function with respect to $\theta$, guiding how parameters should shift to minimize the loss.

Model updates based on operator feedback and post-event analysis can be seen as a specialized form of online learning, where feedback serves as “soft labels” or directly influences the adjustment of the loss function. Assuming operator feedback or analytical outcomes can be quantified into a correction term ${c_t}$, the loss function can be adapted as follows:$l^{\prime}(f({x_t};\theta ),{y_t})=l(f({x_t};\theta ),{y_t})+{c_t}$

The term ${c_t}$ might be weighted according to the accuracy, significance, or urgency of the alert response, guiding the model’s learning. Subsequently, akin to the gradient update rule outlined earlier, yet based on the modified loss l’, parameters are optimized.

For instance, within a state monitoring and early warning system, if an alert is confirmed as a false alarm, the system can incorporate negative feedback ${c_t}$ for such instances, reducing the likelihood of similar false alarms in future model training. Conversely, if an alert successfully averts an incident, positive feedback reinforces the learning effect, enhancing the accuracy of alerts and overall system reliability.

Through the formulaic exposition above, we gain a clearer understanding of how the online learning framework and feedback-based optimization strategies practically reinforce the continuous learning capacity of the system, ensuring the monitoring and early warning system dynamically adapts to changes in the power grid, consistently enhancing its performance.

Based on the intelligent scheduling algorithm described above, the engine needs to integrate DDQN (Deep Deterministic Policy Gradient) and attention mechanism, and ensure the efficiency of the algorithm through highly optimized code implementation. The optimization objective of DDQN algorithm is to minimize the loss of Q value function, i.e., where is the current state, is the current action, is the reward, s’is the next state, is the discount factor, and is the Q value under the current policy is the parameter of the target network. In addition, a dynamic adjustment mechanism of algorithm parameters should be established to automatically adjust parameters according to the operation status of the power grid to adapt to the dispatching requirements under different operating conditions. Algorithm optimization also includes periodic review and iterative updating of the model to ensure that the scheduling strategy is always in the best state. (He et al. 2019)

The intelligent dispatching engine needs to have efficient data processing capability, integrate data from multiple sensors and substations in real time, and perform necessary preprocessing and format conversion. Decision logic needs to be designed flexibly, and can generate dispatching instructions quickly according to many factors such as actual load, fault state and maintenance plan of power grid. In addition, fuzzy logic, rule engine and other technologies are introduced to assist algorithms to make more realistic decisions (Lei et al. 2022).

Condition monitoring and early warning system is the first line of defense to prevent power grid accidents. By monitoring the operation status of power grid in real time, potential problems can be found in time and early warning can be provided for dispatching decision-making. The system needs to integrate data of various monitoring equipment, such as current, voltage, temperature, equipment status, etc., and process and analyze massive data in real time through big data analysis technology, such as time series analysis, pattern recognition, etc., to extract key information. Multi-dimensional data fusion helps to build a more comprehensive view of the state of the grid and improve the accuracy of anomaly detection (Lu et al. 2019).

When building the anomaly detection model, we used a variety of machine learning algorithms, including Isolation Forest, Support Vector Machine (SVM), and Deep Learning, to improve the speed and accuracy of identifying behaviors that deviate from normal operating patterns.

Isolated Forest Algorithm This is an unsupervised algorithm for anomaly detection that is suitable for high-dimensional datasets. The basic idea is to “isolate” observations by randomly selecting features and segmentation values. The fewer partitions an observation needs to be isolated, the more likely it is to be an outlier. The outlier score for the Isolated Forest algorithm is given by: where is the outlier score for the sample, is the average of the path lengths in the isolation tree, and is the correction factor used to adjust the path lengths. SVM is a supervised learning algorithm that can be used for classification and regression problems. In classification problems, SVM separates data points of different classes by finding hyperplanes with maximum separation. The goal of SVM is to minimize generalization error, which can be expressed by the following formula: where w is the normal vector of the hyperplane, b is the bias term, is the input data point, is the class label of the data point, and n is the number of data points.${\text{subject to}}\quad {y_i}({w^T}{x_i}+b) \geqslant 1,\quad i=1,.,n$Deep learning models, such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs), can identify anomalies by learning complex patterns in large amounts of historical data. For example, a simple multilayer perceptron (MLP) can minimize the loss function through a backpropagation algorithm as follows: where m is the number of samples, is the true label, is the probability predicted by the model, and is the model parameter (Li et al. 2022a, b, c).

To improve the model’s ability to identify new types of anomalies, we employ a continuous learning process in which the model continuously learns new historical data and known failure cases. In addition, in order to enhance the interpretability of the model, we designed a mechanism to enable operations personnel to understand the logic behind the alert and respond effectively. This includes providing detailed information about each alert, such as the importance of the feature that triggered the alert, historical similarities, etc., as well as visualizing tools to help operations staff intuitively understand the decision-making process of the model. Through these methods, we not only improve the prediction accuracy of the model, but also enhance the trust and dependence of the operation personnel on the model.

According to the severity and possible impact range of the anomaly, the system needs to automatically classify the warning level and match the corresponding response strategy. Low-level warnings may only need to be recorded and monitored, while high-level warnings need to be notified immediately to the dispatch center to initiate emergency plans. Early warning information shall be displayed through intuitive interface, including but not limited to sound and light alarm, SMS, email notification, etc., so as to ensure rapid communication of information. Establish an early warning effect evaluation mechanism to evaluate the accuracy and timeliness of the early warning system by comparing actual events with early warning records. According to the evaluation results, the early warning model and threshold setting are continuously optimized to form a closed-loop feedback mechanism to continuously improve the early warning effectiveness of the system.

The development of core modules focuses on the construction of intelligent scheduling engine and condition monitoring and early warning system, and the efficient interaction between the two is the key to ensure the efficient and safe operation of power grid. As shown in Fig. 4, the intelligent scheduling engine, as the center of the system, is closely connected with the state monitoring and early warning module to realize the two-way flow and processing of real-time data. In the process of development, the challenges are mainly focused on the design of communication protocol between modules, the efficiency of data synchronization and the guarantee of high availability of modules. In order to overcome these challenges, we adopt the asynchronous communication mechanism based on message queue, which ensures the high efficiency and low latency of data exchange. At the same time, a micro-service architecture is introduced, so that each module can be independently deployed and expanded, and the fault tolerance and the stability of the system are enhanced. In addition, through the standardized design of API interface, the seamless access of new modules and smooth upgrade of old modules are ensured, laying a solid foundation for continuous iteration and optimization of system. In the module integration test stage, we verify the fluency of the cooperation between modules and the robustness of the whole system by simulating various power grid operation scenarios, and prove the effectiveness and practicability of the design.

Performance evaluation and case studies

Construction of evaluation index system

In order to comprehensively evaluate the performance of the system, we construct an evaluation index system including four dimensions: technical performance, economy, reliability and user experience.

Table 1 shows the evaluation index system of the main distribution network integrated network command system, which covers four dimensions: technical performance, economy, reliability and user experience. Each dimension contains specific indicators, such as response time, data processing speed, system stability, automation degree, initial investment cost, annual operation and maintenance cost, payback period, failure rate, system availability, safety protection level, operation convenience score, user satisfaction survey and training demand. These indexes can evaluate the performance and practical application value of the system comprehensively.

Table 1 Evaluation index system of main distribution network integrated network command system

To ensure the comprehensiveness and usefulness of the evaluation system, clear benchmarks have been set for each indicator. In terms of technical performance, it leads the industry standard with a response time of less than 2 s; the data processing rate reaches 10,000 pieces/second, which is comparable to the performance of the top energy system; the system stability pursues a high availability rate of 99.99%; and the automation level target is more than 90% task automation. In terms of economy, the initial investment cost is 10% lower than the industry average, the annual operation and maintenance cost is reduced by 5%, and the cost is recovered within 5 years. Reliability: failure rate less than 0.1 times/year, well below industry average; system availability up to 99.999%, meeting the most stringent standards; compliance with NERC CIP or ISO 27,001 for safety. In the user experience dimension, the average score of ease of operation shall not be less than 9 points; the user satisfaction shall exceed 95%; and the average training time shall not exceed 8 h.

Data collection and analysis will combine quantitative and qualitative methods: real-time performance data will be obtained using system logs and monitoring tools; financial records will assist cost-benefit analysis; user surveys and interviews will collect subjective feelings and quantify satisfaction through statistical means; training effectiveness will be evaluated through pre-and post-test; reliability indicators will be analyzed in depth based on failure records and system operation logs using survival analysis methods. Continuous real-time and periodic data analysis will enable rapid detection and correction of performance deviations, driving continuous system optimization.

Simulation experiment design and result analysis

To verify the system performance, we design a series of simulation experiments to simulate the power grid operation state under different load conditions, focusing on the response ability and stability of the system under high concurrent requests. Experimental results are summarized in Tables 2, 3, 4 and 5.

Table 2 shows a comparison of the response time of the system under different loads. As can be seen from the table, the average response time of the system increases with the increase of load level, but under medium and low load, the response time of the system is shorter and the performance is excellent.

Table 2 Comparison of response time under different loads

Table 3 shows the test results for data processing rates. With the increase of data volume, the processing rate decreases, but even when processing 100,000 data, the system can still maintain the processing rate of 300/s, showing a strong data processing capacity.

Table 3 Data Processing Rate Test results

Table 4 shows the observation record of system stability. During 90 days of operation, the system was interrupted twice, and the stability index reached 99.97%, which indicated that the stability of the system was very high and the number of long-term operation interruptions was very small.

Table 4 System Stability Observation Record

Table 5 shows the simulated attack test results for security protection. The success rate of the system against SQL injection and XSS attacks reached 100%, but the success rate against DDoS attacks was 80%, indicating that the system has high defense capabilities in the face of common network attacks, but there are still some challenges in dealing with large-scale DDoS attacks.

Table 5 Security Protection Simulation Attack Test

From the above experimental results, it can be seen that the system performs well under medium and low load, with short response time and strong data processing ability; although it decreases under high load, it still maintains a high level.

However, there are certain limitations and assumptions in the simulation design. First, load condition simulations focus on theoretical distributions and do not cover extreme or abnormal load cases, which may underestimate the fluctuations in system performance in real complex environments. Secondly, although the test of data processing capacity shows good processing speed, the complexity and diversity of data in practical applications may lead to a decrease in processing efficiency. With regard to system stability observations, a 90-day observation period may not be sufficient to reflect all possible failure modes, especially those with very low frequency but significant impact. In the security protection capability test, although the success rate of defense against common attacks is high, the lack of defense against DDoS attacks reveals shortcomings in the system’s response to large-scale malicious traffic, which is especially important in the context of today’s increasingly complex network security threats.

Statistical analysis showed that the system performed well on most metrics, but needed to be more resilient in the face of heavy load handling and extreme safety challenges. Future work should consider introducing more extensive load stress testing, increasing the tracking and evaluation of long-term stability of the system, and strengthening the development and integration of defense technologies for new and high-intensity network security attacks to further improve the overall performance and reliability of the system.

Case analysis of practical application scenarios

Taking a city power grid as an example, after implementing the integrated network command system of main distribution network, the dispatching efficiency of power grid is significantly improved, the fault response time is shortened by 30%, and the power outage time of users is reduced by 25%. By comparing the data of one year before and after implementation (see Table 6), it can be seen that the system has a direct impact on improving power supply reliability and service quality.

Table 6 Comparison of power grid operation data before and after implementation

Table 6 shows the comparison of power grid operation data before and after the implementation of the main distribution network integration network command system. After implementation, the fault response time is shortened by 30%, and the user outage time is reduced by 25%, which directly reflects the impact of the system on improving power supply reliability and service quality.

Moreover, the economic benefits are notable, with operational and maintenance costs seeing a decline of 15% annually due to the system’s enhanced efficiency and predictive maintenance capabilities. This reduction translates to substantial cost savings for utility providers, which can be redirected towards infrastructure upgrades or customer services.

In terms of scalability, the system has demonstrated adaptability across varying scales and operational contexts. A subsequent pilot in a rural area with dispersed power infrastructure observed a similar reduction in outage durations and an improvement in emergency response times despite the unique challenges posed by remote locations. This underscores the system’s flexibility in integrating with existing grid infrastructures and its capability to optimize resource allocation regardless of geographical constraints.

Furthermore, cross-regional compatibility assessments revealed that the modular design of the integrated network command system facilitated easy customization and integration with regional-specific grid management systems. By adjusting parameters and incorporating local operational rules, the system can be effectively deployed in different regions or countries, thereby enhancing the global applicability of this smart grid solution.

In conclusion, the practical application not only validates the efficacy of the system in enhancing grid efficiency and reliability but also underscores its significant potential for scalable deployment across diverse power grid environments and operational contexts worldwide.

Performance comparison with traditional systems

At last, we compare the performance of the system with traditional dispatching system. The results show that the new system has significant advantages in terms of response speed, automation level, economy and user experience.

Table 7 Performance comparison between old and new systems

Table 7 shows the performance comparison between the old and new systems. The new system has significant advantages over the traditional system in response time, automation degree, annual operation and maintenance cost and user satisfaction, especially in response speed and user satisfaction. The improvement range of the new system reaches 75.7% and 28.6% respectively, showing a significant improvement in performance of the new system.

However, transitioning from traditional systems to the new integrated network command system poses several challenges. Firstly, staff training is crucial to ensure operators can effectively utilize the advanced automation features and navigate the new interface, which may initially face resistance due to a learning curve and change aversion. Secondly, integration complexity arises from connecting legacy infrastructure with modern technology, requiring careful planning and potentially significant software and hardware upgrades.

Economically, while the new system promises a 20% reduction in annual operation and maintenance costs, upfront investment for system upgrade or replacement can be substantial. Thus, a comprehensive cost-benefit analysis is vital to secure management buy-in and budget allocation. Lastly, ensuring data compatibility and cybersecurity during the transition is paramount, given the heightened risk of cyber threats with increased system interconnectedness.

Despite these challenges, the long-term benefits of improved operational efficiency, cost savings, and enhanced user satisfaction make the transition a strategic imperative for modernizing power grid management. Continuous monitoring and iterative improvements post-implementation will further mitigate risks and optimize system performance.

Conclusion

The development and implementation of the integrated network command system of main distribution network marks a great progress in the field of power system dispatching, and successfully integrates modern information technology with power grid management. The system not only achieves innovation at the technical level, such as adopting hierarchical architecture to ensure efficient operation of the system, integrating the latest ICT technology to ensure real-time and security of data transmission, but also significantly improves the accuracy and response speed of decision-making by introducing intelligent scheduling algorithms. The fine design of intelligent dispatching engine and condition monitoring and early warning system ensures the stability and predictability of power grid operation, effectively reduces fault response time and user outage frequency, and improves the overall power supply reliability and service quality. In practical application, the system significantly reduces operation and maintenance costs and improves the efficiency of grid resources through highly automated and intelligent scheduling strategies. At the same time, the substantial increase in user satisfaction reflects the success of the system in terms of human-computer interaction and operational convenience. Performance evaluation results show that the new system is superior to the traditional scheduling system in many key indicators, especially in response time, automation, economy and user experience, which proves the rationality and effectiveness of the system design.

Our work on grid management has made strides, but there’s room for improvement. Predictive analytics and machine learning need broader testing for accuracy in fault prediction and grid optimization. Cybersecurity must adapt to new threats. Scalability should include blockchain for data integrity and IoT for better control. Environmental sustainability requires integrating renewables and reducing carbon emissions. The human element should be considered for smooth transitions to new systems, perhaps using VR and personalized training. Overall, ongoing innovation is key to resilient, sustainable, user-friendly smart grids that incorporate technology, economics, environment, and social aspects.