Design of an integrated network order system for main distribution network considering power dispatch efficiency

Abstract

This study presents a comprehensive review of the primary distribution design of an advanced network control system, emphasizing its evolution from initial requirements to practical applications. The system solves complex problems of power management by combining real-time data analysis, intelligent decision making for resource allocation, rapid fault correction, remote monitoring and complex optimization methods, all aimed at ensuring stable and safe operation of the power grid. Its performance is geared towards fast response, efficient data processing and synchronous processing tasks, ensuring smooth operation even under heavy workloads. Security is enhanced through strict protocols, encryption methods, and controlled access systems. The system is divided into four layers-data collection, communication, decision-making and application management-using innovative tools such as Kalman filters and deep Q networks. The research showcases the integrated network command system’s prowess, achieving an average response time of 0.27 s, 98.5% dispatching accuracy, and 83.2% resource utilization, evidencing exceptional performance. It excels under various tests, including managing high loads with minimal accuracy loss, rapidly adapting to changes with a hydro model response time of 0.22 s, efficiently integrating renewables at 78.0% efficiency, and proving resilient in peak hours, affirming its capability to bolster grid efficiency, reliability, and integration of renewable energy resources. By outlining these specific achievements, this case study not only illustrates the complex design of the system, but also highlights its great potential for improving grid resilience and efficiency, attracting a wide audience interested in the future of energy management.

Introduction

Under the background of global economic development and accelerated population growth, the world energy consumption has increased unprecedentedly, and the demand for electricity has occupied a key position. This escalating dependence highlights the deep-rooted dependence of modern society on energy resources, while posing significant challenges to existing power infrastructure. The international community, and China in particular, faces an urgent need to strengthen systematicness to respond to the dynamics of this new era.

The integration of distributed energy sources (DER), especially solar and wind, has triggered a paradigm shift in grid operations, bringing opportunities and challenges. While DER promises sustainability, its intermittent nature complicates grid management and requires complex strategies to reconcile fluctuating supply with changing demand patterns. Traditional power dispatch systems characterized by centralized architectures tend to hesitate to quickly adapt to these rapid changes, resulting in delays and inefficiencies. This highlights the urgent need for transformative reforms aimed at improving the responsiveness and efficiency of dispatch mechanisms (Li et al. 2022a, b, c, 2024).

The pursuit of a smarter and more sustainable grid requires an integrated approach that leverages technological innovations such as artificial intelligence, cloud computing, and big data analytics. Worldwide research, particularly in technologically advanced regions such as Europe and North America, has focused on implementing smart grid technologies and advanced distribution management systems (ADMS) to simplify grid operations. Meanwhile, in China, research efforts have focused on large-scale integration of ultra-high voltage (UHV) grids and renewable energy, exploring how advanced technologies can be integrated into dispatch systems to optimize energy flows and enhance system resilience (Xi et al. 2018; Lu et al. 2019).

This study contributes to this global discourse by designing an innovative main distribution network command system designed to overcome contemporary dispatching challenges. The system is carefully designed around three core pillars: a high-speed information exchange platform that utilizes advanced data fusion and communication protocols for real-time data exchange; an active scheduling algorithm system that dynamically adapts grid operations through machine learning to ensure accurate power balancing and resource allocation in real time; and a robust security infrastructure that includes prevention, detection, and response measures and is reinforced by rapid response mechanisms to reduce outages and ensure uninterrupted power supply. This holistic system design aims to revolutionize grid scheduling by dramatically improving efficiency, intelligence, and reliability (Liu et al. 2020; Sharma and Mishra 2022; Vahid-Pakdel and Mohammadi-ivatloo 2018).

The overall goal of this study is to achieve a transformative leap in dispatch efficiency and accuracy, thereby significantly reducing response cycles, controlling operating costs, and enhancing the grid’s Incident Response Service and resilience. Through rigorous application and validation of real-world case studies, we hope not only to demonstrate the feasibility and effectiveness of our systems, but also to provide actionable insights and theoretical frameworks to guide the development of smarter, more agile, and more reliable smart grids worldwide. Our work aims to facilitate the transition to a future-proof, adaptable power infrastructure that seamlessly integrates modern energy needs and the complexity of renewable energy.

Theoretical basis of command system of integrated network of main distribution network

Power system basics

Electric power system is the lifeblood of modern society, responsible for the production, transmission, distribution and consumption of electric energy. Its complex and sophisticated structure supports the normal operation of modern society. Power system can be roughly divided into five links: generation, transmission, transformation, distribution and consumption. The power generation link is mainly composed of various power stations, including thermal power, hydropower, nuclear power, wind power and solar energy, and is responsible for converting primary energy into electrical energy; the transmission and transformation link is responsible for long-distance, high-voltage power transmission, and when necessary, the voltage level is changed to meet different needs; the distribution link converts high-voltage electricity into low-voltage electricity suitable for users and distributes it to thousands of households; the electricity consumption link is the terminal of the power system, covering various electrical equipment and users (Zhang et al. 2022).

The operation principle of power system revolves around the core goal of balancing power supply and demand, which ensures that the power generation of the system matches the load demand at any time. To achieve this balance, power dispatch plays a crucial role. The basic requirements of power dispatching include, but are not limited to: ensuring safe and stable operation of the system, avoiding overload and frequency fluctuations; optimizing resource allocation, improving economy, minimizing generation costs; and responding to load changes and quickly handling emergencies.

The electric power transportation process is shown in Fig. 1.

Fig. 1

Electric power transportation process

At the core of an electric power system lies the transformation of primary energy into electricity through various generation methods. Thermal plants burn fossil fuels or utilize nuclear reactions to heat water into steam, driving turbines, while hydroelectric facilities convert the energy of falling water into electricity. Renewables, such as solar PV and wind turbines, harness natural resources directly. Electricity then travels via high-voltage transmission lines from power plants to substations, stepped up by transformers to minimize loss. Substations act as critical junctions, adjusting voltage levels for efficient distribution or further transmission, equipped with switchgear for rerouting and protective measures against faults.

In the distribution phase, voltage is lowered for delivery to consumers via a widespread network of transformers and power lines. End-users consume this electricity for diverse applications, from domestic lighting to industrial processes. Advancements include smart meters and demand-side management systems, fostering efficiency and grid communication.

Integral to system integrity are protection and control systems. Circuit breakers isolate faults, relays detect anomalies, and SCADA systems enable remote monitoring and adjustment for operational safety and reliability. A graphical representation would showcase the seamless flow of electricity from generation to consumer, under the watchful eye of control centers adjusting parameters based on real-time data for optimal grid performance.

Integrated network command technology

The core of this technology is to build a unified command and information exchange platform, so that the main network dispatching instructions can be directly issued to the distribution network level, even specific distribution equipment, to achieve the optimal allocation of resources and efficient execution of dispatching instructions within the whole network (Alazemi et al. 2022; Dabbaghjamanesh et al. 2020).

Fig. 2

Integrated command system

The concept of integrated command system is not only limited to the integration of technical level, but also an innovation of management concept. It emphasizes the transparency of information, directness of instructions and agility of response. Through a highly integrated information system, real-time monitoring of power grid status, rapid response to internal and external changes of power grid, optimization of power flow and improvement of overall system efficiency are shown in Fig. 2. As shown in Fig. 2, the system structure is complex, covering the entire power supply chain from generation to management. Dispatching power plants are located at the upper part of the data network area on the left to monitor the power generation process. The intelligent operation and information display area in the middle includes an information ordering platform 1 and an information ordering platform 2, which respectively process orders, inventory management and other services. The electricity distribution network at the bottom is responsible for transporting electricity everywhere. The command and control platform on the right coordinates and manages the operation of the entire system, while the management system on the far right integrates all platforms and components for overall planning and optimization.

The advantages are embodied in many aspects: firstly, it improves the timeliness and accuracy of dispatching, reduces the intermediate links, reduces the delay and bit error rate of information transmission; secondly, it enhances the flexibility and adaptability of the power grid, especially in the face of distributed energy access and rapid load changes, and can quickly adjust the strategy to maintain the stability of the power grid; Finally, it promotes the optimal allocation of resources, maximizes the use of available resources through global perspective scheduling, and reduces energy consumption and operating costs (Chen et al. 2019).

Compared with the traditional power dispatching system, the integrated network command technology shows significant progress and optimization, mainly reflected in the following core aspects:

Real-time response and adaptive capability: Traditional dispatching systems often rely on manual monitoring and decision-making, and the response speed is limited by human processing time, while integrated command systems can realize millisecond automatic response and strategy adjustment through integrated advanced algorithms, artificial intelligence and Internet of Things technology. This not only significantly shortens the fault handling cycle, but also enables power matching and resource reallocation immediately when demand fluctuates, enhancing the dynamic adaptability of the system.

Precise scheduling and optimization: Traditional systems are often scheduled based on fixed models and historical data, making it difficult to accurately predict and respond to complex changes in grid conditions. In contrast, the integrated system uses machine learning and big data analysis to learn the power grid operation mode in real time and realize more accurate load forecasting and generation planning, thus improving the dispatching accuracy to more than 98.5%, which is much higher than the average level of traditional systems.

Resource utilization and economic efficiency: Traditional scheduling may lead to uneven resource allocation and low utilization due to information processing delay and inaccurate scheduling. Through intelligent optimization algorithms, the integrated system can efficiently integrate various energy resources, especially intermittent access to renewable energy, increasing the overall resource utilization rate to 83.2%, which is significantly higher than 75% of the traditional system, and reducing operating costs.

To sum up, the integrated network command technology greatly improves the response speed, scheduling accuracy, resource utilization rate and system stability of power dispatching through highly automated and intelligent means, providing powerful technical support for building a more flexible, reliable and efficient modern power grid, marking an important leap in the field of power dispatching.

Intelligent optimization algorithm

In order to further improve the intelligent level and efficiency of power dispatching, intelligent optimization algorithms are widely used in power system dispatching. Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are two commonly used heuristic search algorithms (Venegas-Zarama et al. 2022).

Genetic algorithm (GA) uses the principles of heredity and natural selection for reference in biological evolution in nature, and searches for the optimal solution by simulating the process of genetic variation, crossover and selection of population. In the field of power dispatching, GA is often used for unit commitment optimization, load forecasting and other problems. It can deal with multi-objective, nonlinear and complex constrained optimization problems and find global optimal solutions.

Particle swarm optimization (PSO) algorithm is inspired by the foraging behavior of birds. Each particle represents a potential solution. By updating the position of individual optimal solution and group optimal solution, particle swarm optimization is guided to approach the global optimal solution. PSO algorithm is simple in calculation and fast in convergence speed. It is suitable for scheduling optimization of large-scale power systems, such as power flow optimization, economic scheduling, etc.

These two algorithms, as well as other intelligent optimization methods such as simulated annealing algorithm and ant colony algorithm, provide powerful calculation tools for power dispatching by introducing randomness and intelligent search mechanism, which makes it possible to formulate dispatching strategies more efficiently and flexibly in the face of complex and dynamic power grid environment, thus improving the overall performance and economy of power system.

Intelligent optimization algorithms are pivotal in advancing the precision and efficiency of power dispatch strategies. The Genetic Algorithm (GA), emulating nature’s evolutionary processes, employs principles of heredity, mutation, crossover, and selection to iteratively refine a population of potential solutions. Mathematically, the fitness function f(x) evaluates each candidate solution x, and the selection process is guided by probabilities derived from relative fitness values. The crossover rate ${p_c}$ and mutation rate ${p_m}$ determine the rates of genetic material exchange and random changes, respectively, in the algorithm’s iterative cycle (Venegas-Zarama et al. 2022).

The Particle Swarm Optimization (PSO) technique, inspired by flocking behaviors, maintains a swarm of particles representing potential solutions. Each particle updates its position based on its own best position ${P_{best}}$ and the swarm’s best position ${G_{best}}$, with velocity v adjusted according to:${v_{ij}}(t+1)=w \cdot {v_{ij}}(t)+{c_1}{r_1}(P_{{ij}}^{{best}}(t) - {x_{ij}}(t))+{c_2}{r_2}({G_{bestj}}(t) - {x_{ij}}(t))$

Where w is the inertia weight, ${c_1}$ and ${c_2}$ are acceleration coefficients, and $({r_1},{r_2})$ are random numbers (Venegas-Zarama et al. 2022). PSO excels in handling large-scale optimization tasks in power systems, such as power flow optimization.

Real-world examples abound, with GA successfully deployed in unit commitment problems at utilities, optimizing generator schedules to balance demand and minimize costs. Similarly, PSO has proven effective in enhancing economic dispatch strategies by dynamically adjusting generator outputs for maximum efficiency, demonstrated in case studies across multiple continents (He et al. 2018).

Research status of command system of integrated network of main distribution network

With the development of smart grid and the deepening of power market reform, the research on the command system of main distribution network integration has become an important direction to improve the efficiency and intelligence of power grid dispatching. In recent years, scholars and practitioners have extensively explored system design, key technologies, practical applications and challenges, and formed a series of research achievements and practical experiences. This section will summarize the current research status of the integrated network command system of main distribution network with several references (He et al. 2018).

Existing studies show that the design of an integrated network initiation system for the main distribution network is usually based on an advanced grid operations management system (OMS) framework, emphasizing the importance of network communication interactions (Mahdad 2019). The system design tends to be integrated and intelligent, such as by establishing an intelligent command platform for distribution network dispatching, and using network communication technology to realize efficient instruction transmission and operation confirmation between dispatchers and field operators (Li et al. 2023). The system functions cover the whole process of network order drafting, third review, order, reply, order receiving, filing, etc., effectively avoiding the disadvantages of traditional telephone orders, such as inaccurate information transmission and low efficiency (Khalid 2019). With the help of big data and cloud computing technology, the system can process a large amount of heterogeneous data for accurate load forecasting and fault diagnosis (Gao et al. 2023). For example, the fault location and isolation strategy optimized by machine learning algorithm significantly improves the speed of Incident Response Service (Zou et al. 2018). ADMS, as a core component, not only improves the automation and intelligence level of distribution network management, but also enhances the coordination capability between main distribution networks, realizing comprehensive scheduling control from macro to micro (Zhou et al. 2020). The application of high-speed communication technologies such as 5G and optical fiber communication provides near-real-time data transmission capability for the system, ensuring immediate transmission and execution feedback of scheduling instructions (Jin et al. 2018). The practical application case shows that the integrated network command system significantly improves the efficiency and security of power grid dispatching. The system can quickly respond to the change of power grid state, effectively shorten the fault handling time and improve the power supply reliability (Mahzouni-Sani et al. 2019). For example, through the integrated scheduling instruction processing flow, human error is reduced and work efficiency is improved (Fabietti et al. 2018). Despite significant progress, the integrated network command system of the main distribution network still faces many challenges. The primary problem lies in the complexity of system integration, especially interoperability and data compatibility between different levels and different vendors ‘equipment (Hassan et al. 2024). Secondly, network security and privacy protection have become increasingly prominent issues, and how to ensure information security in the process of instruction transmission has become the focus of research (Guan et al. 2022). In addition, the adaptability of policies and regulations is also one of the constraints, requiring system design to have sufficient flexibility to respond to changes in market rules (Xu et al. 2020).

As a key tool to improve the intelligence and efficiency of power grid dispatching, Integrated Network Command System (INCS) shows diversified designs and applications. For example, systems based on the Advanced Grid Operations Management System (OMS) framework emphasize the interactive nature of network communication, utilizing intelligent command platforms and high-speed communication technologies such as 5G and fiber optic communications to achieve immediate and error-free communication of dispatch commands, significantly improving the limitations of traditional telephone commands, such as misinformation and inefficiency. The integration of Advanced Distribution Management System (ADMS) not only enhances the automation and intelligence level of distribution management, but also promotes the coordination ability among main distribution networks, and realizes the overall dispatching control from macro to micro (He et al. 2019). These systems process heterogeneous data through big data analytics and cloud computing, accurately predict loads and quickly diagnose faults, improving the response speed and power supply reliability of the power grid. However, each system also has limitations, mainly including: high system integration complexity, data compatibility and interoperability between different levels and suppliers ‘equipment need to be solved urgently; accompanied by digital upgrading, network security and personal privacy protection are severe challenges, ensuring information security in the process of instruction transmission has become a research focus; In addition, the adaptability of policies and regulations also restricts system design, requiring higher flexibility to cope with changes in market rules. Future developments will therefore focus on how to deepen the convergence of AI technologies, promote cross-level collaboration and integrated energy system construction, and strengthen standardization and modular design to reduce integration costs and accelerate the application of technological innovation (Li et al. 2022a, b, c).

The ongoing evolution of smart grid technologies and the intensification of power market liberalization have accelerated the development of integrated network command systems for main distribution networks. Recent research has pivoted towards enhancing interoperability among hierarchical systems and supplier equipment, with a focus on data harmonization and standardized communication protocols (Huang et al. 2021). One significant advancement lies in the integration of reinforcement learning within dispatch algorithms, allowing systems to learn optimal strategies based on real-time grid conditions and historical outcomes. This approach has shown promise in managing distributed energy resources, enhancing grid flexibility and resilience (Li et al. 2022a, b, c). Challenges persist, however, primarily centered around cybersecurity threats, which have escalated with the proliferation of IoT devices and increased data exchange. Advanced encryption methodologies and intrusion detection systems are being actively researched to fortify system defenses (Lei et al. 2022). Moreover, the integration of renewable energy sources, with their inherent variability, necessitates sophisticated forecasting algorithms and adaptive control mechanisms, spurring research into hybrid forecasting models and advanced control theories. Regulatory frameworks must keep pace with technological advancements. Efforts are underway to develop flexible policies that encourage innovation while ensuring grid stability and consumer protection. Modular system designs and open-source platforms are being explored to expedite technology adoption and facilitate cross-system collaboration (Makeri 2020).

In the future, the research direction will focus on deep integration of artificial intelligence technologies, such as reinforcement learning applied to scheduling strategy optimization; promoting cross-layer collaboration and integrated energy system construction; and strengthening standardization and modular design, reducing integration costs and accelerating technological innovation applications.

System requirements analysis and design framework

System requirements analysis

The effective operation of power dispatching system is the foundation to ensure the safe and stable operation of power grid. Facing the increasingly complex power grid environment, the integrated network command system of main distribution network needs to fully consider the requirements of function, performance and safety. The specific requirement framework of the system is shown in Fig. 3.

Figure 3 shows the two core categories of requirements for building an integrated network command system: performance requirements and security requirements. In terms of performance, system response time, data processing throughput, and concurrency are critical factors. System response time refers to the time interval from receiving a request to generating a response, reflecting the rapidity and real-time nature of the system; data processing throughput measures the amount of data that the system can process per unit time, reflecting the processing efficiency of the system; concurrent processing capability refers to the ability of the system to process multiple tasks simultaneously, ensuring the effectiveness of multi-task parallel processing. In the aspect of security, encryption transmission, access control mechanism and multi-level defense system constitute the basis of information security. Encrypted transmission ensures the safe transmission of data in the network to prevent illegal theft or tampering; access control mechanism restricts unauthorized access to sensitive information and protects the privacy of data; and multi-level defense system is to resist external attacks and internal vulnerabilities through multi-level security protection measures, enhancing the overall security of the system.

Functional requirements: The system shall have real-time data acquisition and processing capabilities to realize seamless connection of main distribution network data; provide intelligent dispatching decision support to automatically adjust power generation and transmission and distribution strategies according to real-time state of power grid; have fault warning and rapid response mechanism to ensure rapid positioning and measures in case of power grid fault; support remote monitoring and control to realize visual management of equipment state; and integrate optimization algorithm to improve power quality and economy (He et al. 2019).

Fig. 3

Requirements framework

Performance requirements: The response time of the system shall be controlled at the second level or even millisecond level to meet the needs of rapid scheduling; the data processing throughput shall meet the real-time processing requirements of large-scale power grid data, expressed by the formula, where is the number of data points and the sampling frequency; the concurrent processing capability of the system shall be achieved to ensure that multiple devices are connected without blocking at the same time, which is the number of devices online at the same time (Li et al. 2022a, b, c).${T_{res}}$${D_{th}}$${D_{th}} \geqslant {N_{data}} \times {F_{sample}}$${N_{data}}$${F_{sample}}$${C_{cap}} \geqslant {N_{device}}$${N_{device}}$

Security requirements: The system shall comply with the network security level protection standard to realize data encryption transmission, in which the encryption strength shall meet, representing the potential attack strength; set the access control mechanism to ensure that only authorized users can access the system; establish a multi-level defense system, including firewall FW, intrusion detection system IDS, etc., to indicate the intrusion detection rate, and ensure a high degree of security protection (Huang et al. 2021).${E_{data}}$${S_{enc}}$${S_{enc}}>{S_{attack}}$${S_{attack}}$${A_{control}}$${P_{detect}}$${P_{detect}} \approx 1$

The main distribution network command system needs to balance function, performance and safety. Real-time data processing requires high-speed interfaces and powerful algorithms, requiring high-performance computing resources and parallel processing to maintain fast response times. Intelligent dispatching automatically optimizes the grid with machine learning, increasing computational requirements. Remote monitoring relies on low-latency communications and advanced network protocols to ensure visibility of equipment status. In terms of security, it follows NIST standards, implements AES-256 encryption, multi-layer defense including multi-factor authentication, access control, IDS and FW, supplemented by regular audits, vulnerability scanning and security training to ensure system resilience.

System architecture design

The hierarchical architecture is adopted in the design of the integrated network command system of the main distribution network, which mainly includes the data acquisition layer, the communication layer, the decision control layer and the application management layer. The specific system framework diagram is shown in Fig. 4.

Figure 4 depicts a complex system architecture consisting of five layers: data acquisition, communication, decision control, application management, and the bottom user interface. Each layer has its own unique role and function. First, let’s look at the data acquisition layer. This layer consists mainly of two types of devices: sensors and smart meters. These devices are responsible for collecting various types of data, such as ambient temperature, humidity, geographical location information, etc. This data is critical for analysis and processing at subsequent levels. Next is the communication layer. In this layer, there are two communication methods: fiber optic communication and 5G wireless communication. Optical fiber communication has the characteristics of high speed and stability, which is suitable for long distance data transmission. 5G wireless communication has higher bandwidth and lower delay, which is suitable for real-time data transmission and processing. Then there is the decision control layer. This layer contains two key components: the big data platform and intelligent scheduling algorithms. Big data platforms are responsible for storing, processing and analyzing the massive amounts of data collected. Intelligent scheduling algorithm optimizes the efficiency and performance of the system according to the analysis results of these data. And then down to the application management layer. This layer has two main parts: the user interface and the Web services architecture. The user interface provides users with a user-friendly interface that enables them to easily manage and configure the system. Web services architecture allows the system to interact with other applications and services to share resources and collaborate. Finally, the user interface layer. This is the final loop of the system and the part with which users interact directly. Through the user interface, users can query system status, set parameters, execute operation commands, etc.

Fig. 4

System framework diagram

Data acquisition layer: deployed at each node of the power grid, using sensors and smart meters to collect real-time data such as voltage, current, power, etc., using IoT technology to achieve data aggregation. Data formats are standardized, following communication protocols such as IEC 61,850, facilitating cross-platform exchange.

Communication layer: High-speed, low-latency communication technologies, such as optical fiber communication, 5G wireless communication, etc., are adopted to ensure efficient data transmission. OSI model is adopted in the design of communication protocol stack to ensure the integrity and reliability of data.

Decision control layer: Based on the big data platform Hadoop/Spark, integrated intelligent scheduling algorithms, such as Fuzzy Logic-based Scheduling (FLBS), the formula can be expressed as, to achieve optimal allocation of resources.$Optima{l_{Dispatch}}=FLBS(Powe{r_{Demand}},Generatio{n_{Capacity}},Gri{d_{Status}})$At the same time, this layer is responsible for fault diagnosis and command generation, and quickly responds to power grid events through decision tree algorithm Decision Tree, DT.

The selection of technologies and protocols in each layer of the integrated network command system for the main distribution grid is grounded in operational efficiency, interoperability, and resilience.

At the Data Acquisition Layer, IoT devices like sensors and smart meters leverage technologies that support the IEC 61,850 standard for uniform data formatting. This facilitates seamless integration and communication across different grid nodes, ensuring that vital parameters such as voltage, current, and power are reliably captured and aggregated in real-time. The choice of IEC 61,850 is pivotal due to its widespread adoption in the energy sector, enabling interoperability and future-proofing the system.

In the Communication Layer, high-speed technologies including optical fiber communication and 5G wireless networks are employed to guarantee rapid data transmission with minimal latency. By adhering to the OSI model in designing the communication protocol stack, the system ensures not only the speed but also the integrity, security, and reliability of data transmission, which is crucial for maintaining grid stability and responsiveness.

The Decision Control Layer rests on robust big data platforms such as Hadoop or Spark, harnessing their capabilities to handle and analyze massive datasets. Algorithms like Fuzzy Logic-based Scheduling (FLBS) optimize resource allocation dynamically, reflecting operational complexity and uncertainties. The use of a Fuzzy Logic system allows for more adaptive decision-making, considering a range of possibly conflicting factors. Complemented by the Decision Tree algorithm for fault diagnosis and command generation, this layer swiftly reacts to grid events, capitalizing on the algorithm’s ability to process complex scenarios into actionable insights, thereby enhancing overall system responsiveness and reliability.

In summary, these technological choices address the core requirements of an efficient and secure power grid management system, balancing real-time data handling capabilities, effective communication, intelligent decision-making, and robust security measures.

Key technology design

To delve deeper into the technical implementations within our system, we employ two key methodologies: the Kalman Filter for data refinement and Deep Q-Network (DQN) for intelligent decision-making in power dispatching. The Kalman Filter, a staple in data fusion algorithms, plays a pivotal role in enhancing data accuracy and reliability. It operates through a recursive process represented by the equations, where represents the estimation of the system’s state, denotes the error covariance, and is the Kalman gain. This filter dynamically updates estimates based on incoming measurements and prior knowledge, thereby rectifying inaccuracies and boosting the dependability of the data fed into our system (Li et al. 2022a, b, c). In parallel, to optimize power dispatching strategies, we fuse classical methods with avant-garde AI techniques. A notable integration is Deep Reinforcement Learning (DRL), specifically its subset, DQN. DQN harnesses the power of deep neural networks to estimate the Q-value function, tackling the complexity of high-dimensional state spaces inherent to power grid management. Its mathematical essence encapsulates the current grid state (s), which consolidates load levels, generator resources, transmission line statuses, and more; the dispatching actions (a) including power output adjustments and transmission choices; neural network weights (θ) iteratively refined for optimal performance; an immediate feedback reward (r) gauging the short-term impact of actions; a discount factor (γ) balancing immediate and future rewards; and transitions to new states (s’) and actions (a’) for continuous learning. Target network parameters (θ^-) stabilize the learning progression, mitigating training instability (Lei et al. 2022). These sophisticated mechanisms – the Kalman Filter ensuring pristine data integrity and DQN enabling sophisticated, adaptive decision-making – collectively fortify the system’s precision, responsiveness, and overall performance, forming the backbone of our intelligent and efficient power grid dispatching solution.

We use data fusion algorithms, such as Kalman Filter (K), where is the state estimation, is the estimation error covariance, and is the Kalman gain to improve data accuracy and reliability (Li et al. 2022a, b, c).${X_k}=F{X_{k - 1}}+B{u_k}+{w_k}$${P_k}=F{P_{k - 1}}{F^T}+Q{K_k}PH_{k}^{T}$${X_k}$${P_k}$${K_k}$

In the field of intelligent algorithms for optimizing power dispatching policies, we not only integrate the robustness of classical fuzzy logic scheduling, but also deeply explore the cutting-edge applications of Deep Reinforcement Learning (DRL), especially its branch-Deep Q-Network (DQN). DQN uses deep neural network architecture to approximate Q-value function, effectively dealing with high-dimensional state space problems, and its mathematical expression core is:$Q(s,a;\theta ) \leftarrow r+\gamma {\hbox{max} _{a^{\prime}}}Q(s^{\prime},a^{\prime};{\theta ^ - })$Among them, s indicates the current state of the power grid, integrating various factors such as load level, generation resources, transmission line conditions, etc.; a is the dispatching decision action, covering the operation of generation output regulation, transmission path selection, etc.; represents the weight parameter of the neural network, which is continuously optimized through iterative learning; r is the instant feedback reward, which measures the immediate benefit of the system after performing a certain action; is the discount factor, which balances the immediate and future benefits; s ‘and a’respectively represent the new state of the power grid after performing action a and its corresponding optimal action; and are the parameters of the target network, which are used to stabilize the learning process and reduce the training fluctuation (Lei et al. 2022).$\theta$$\gamma$${\theta ^ - }$

The practice of DQN relies on two core mechanisms: one is Experience Replay, which randomly reuses historical interaction data for learning, breaking the dependence of data continuity and improving the stability and generalization ability of learning; the other is Fixed Target Network, which slowly updates the parameters of the target network to slow down the violent fluctuation of Q value estimation and ensure the smooth progress of learning process.

This intelligent dispatching mechanism integrating DQN endows the power dispatching system with unprecedented adaptability and accurate regulation ability, ensuring that under complex and changeable power grid environment, such as rapid load fluctuation and sudden change of power generation resources, the system can quickly evaluate the long-term impact of various dispatching strategies, adopt the optimal decision path, and realize the efficient allocation of power resources and the optimal balance of power grid operation. The introduction of DQN not only significantly improves the efficiency and economy of dispatching, but also strengthens the stability and anti-disturbance ability of power grid, showing the deep insight and high adaptability potential of deep reinforcement learning technology in complex decision-making problems.

Security protection mechanism: Public key infrastructure PKI is adopted to ensure data transmission security, encryption algorithm AES (Advanced Encryption Standard), key length is at least 128 bits, provide, where is encryption function, decryption function, m is plaintext, c is ciphertext, k is key, to ensure data privacy.${E_k}(m)=c$${D_k}(c)=m$${E_k}$${D_k}$At the same time, regular security audits and vulnerability scans are implemented to ensure the safe and stable operation of the system.

Our system fuses the Kalman Filter and DQN, chosen for their excellence in enhancing data integrity and intelligent decision-making. The Kalman Filter, at data acquisition, refines sensor inputs, reducing noise and improving data reliability, which accelerates precise decision-making (Li et al. 2022a, b, c). Integrated DQN, within the decision control layer, uses deep neural networks to navigate complex grid management, adapting to rapid changes, and optimizing resource allocation through advanced learning mechanisms (Lei et al. 2022). Together, they heighten system responsiveness, efficiency, and resilience.

Security is bolstered by a PKI-backed framework with AES-128 encryption, ensuring data privacy and integrity, complemented by regular audits and vulnerability scans (Makeri 2020). This fusion creates a highly efficient, adaptive, and secure power dispatch system, adept at handling modern grid complexities and future challenges, safeguarding a stable power supply infrastructure.

Key module implementation and algorithm design

Data acquisition and preprocessing module

Data acquisition and pretreatment is the basis of the integrated network command system of main distribution network, and its design should ensure the real-time, accuracy and availability of data. First, multi-source heterogeneous data acquisition solutions, including but not limited to smart meters, sensors, SCADA systems, etc., are used to achieve efficient data transmission through industrial Ethernet, wireless sensor networks and other technologies. Data formats follow standard protocols such as IEC 61,850, Modbus, etc. to ensure data compatibility and interoperability.

The data preprocessing process includes three steps: data cleaning, formatting and standardization. Noise filtering algorithm is adopted for data cleaning, such as median filtering method, where is the denoised data point, is the center point, N is the window size, and effectively removes random noise.${y_t}=Median({x_{t - N}},…,{x_t},…,{x_{t+N}})$${y_t}$${x_t}$The formatting process converts raw data into a uniform structured data format for subsequent analysis. Normalization ensures comparability between data from different sources by methods such as normalization or Z-score normalization, where x is the original data, mean and standard deviation.$z=(x - \mu )/\sigma$$\mu$$\sigma$

The data acquisition process initiates with the deployment of multi-source, heterogeneous data collectors, encompassing smart meters, environmental sensors, and Supervisory Control and Data Acquisition (SCADA) systems. These devices transmit data over industrial Ethernet and wireless sensor networks, ensuring real-time transmission. A key challenge lies in managing the heterogeneity of data formats and ensuring seamless interoperability, addressed by adherence to standard protocols like IEC 61,850 and Modbus.

During preprocessing, data cleaning employs advanced algorithms such as adaptive median filtering to dynamically adjust the window size N based on signal characteristics, efficiently removing outliers and noise $({x_{clean}}=Med({x_N})$, where ${x_{clean}}$ denotes the denoised data point, and Med represents the median function applied over a window of N data points. Formatting and standardization procedures transform raw data into a unified structure, followed by normalization to a common scale, e.g., $z=\frac{{x - \mu }}{\sigma }$, where z is the normalized value, $\mu$the mean, and $\sigma$ the standard deviation.

Performance metrics for this module include data completeness, accuracy after cleaning (evaluated via comparison with ground truth or known clean datasets), and preprocessing latency. A success criterion could be achieving over 99% data integrity, less than 1% error rate post-cleaning, and preprocessing latency below 100 milliseconds for real-time operation.

Implementation of intelligent schedule algorithm

In the implementation of intelligent scheduling algorithm, we select deep Q network (DQN) as the core algorithm, the key lies in how to construct and train neural network model. In model design, the network usually consists of an input layer, several hidden layers and an output layer. The input layer receives the state information of the power grid, and the output layer gives the Q value of each possible action. ReLU activation function is used in hidden layer to increase the nonlinear expression ability of the model.

For parameter setting, learning rate, discount factor, initial value of exploration rate ε and decay strategy are considered.$\alpha$$\gamma$In general,, ε is initially set to 0 and gradually decreases the exploration ratio as the learning progresses.$\alpha =0.001$$\gamma =0.95$Experience playback buffer size B and target network update frequency C are also important parameters recommended to balance learning efficiency and stability.$B={10^6}$$C={10^4}$The optimization objective is to minimize the error of Bellman’s equation, i.e., where the network parameters are updated by gradient descent, with the formula.$L(\theta )={{\mathbb{E}}_{(s,a,r,s^{\prime})\sim U(D)}}[{(y - Q(s,a;\theta ))^2}]$$y=r+\gamma {\hbox{max} _{a^{\prime}}}Q(s^{\prime},a^{\prime};{\theta ^ - })$$\theta \leftarrow \theta - \alpha {\nabla _\theta }L(\theta )$

The DQN algorithm’s implementation begins with defining the neural network architecture tailored to the complexity of the power grid’s state space. The model comprises an input layer receiving grid state vectors, multiple hidden layers applying ReLU activations for non-linearity, and an output layer yielding Q-values for each possible action. The network is trained using the Bellman equation’s error minimization objective, with gradients calculated via backpropagation as per the formula $\Delta \theta \propto - {\nabla _\theta }Q(s,a;\theta )(r+\gamma {\hbox{max} _{a^{\prime}}}Q(s^{\prime},a^{\prime};{\theta ^ - }) - Q(s,a;\theta ))$, where $\theta$ represents the network parameters, and ${\theta ^ - }$ denotes the parameters of the target network.

Training involves setting hyperparameters like learning rate $\alpha$, discount factor $\gamma$, and exploration rate $\epsilon$, which starts at 0 and is annealed over epochs. An experience replay buffer of size B stores past interactions, and the target network is updated every C steps to stabilize learning.

For performance evaluation, the algorithm is tested on historical and simulated datasets, measuring key indicators such as convergence speed (measured by episodes until reaching a stable performance threshold), average reward over time, and adaptability to dynamic grid conditions. A successful benchmark might demonstrate convergence within 500 episodes, an average reward increase of 10% compared to traditional methods, and effective adaptation to sudden load variations.

Command execution and feedback module

Fig. 5

Execution and feedback mechanism

Command execution and feedback mechanism is the key to ensure accurate execution of scheduling instructions, involving command issuance, execution monitoring and exception handling. The specific mechanism is shown in Fig. 5. Figure 5 shows the workflow of the Command Execution and Feedback module. First, Command Issuance sends instructions to the execution monitoring and exception handling module via the RabbitMQ message queue. Execution monitoring module is responsible for tracking the status of instructions, the initial state is pending execution, and then enters into execution (In progress). If the execution succeeds, the status changes to Completed, otherwise it checks to see if an exception has occurred. If there is an exception, exception handling strategies are triggered, including retry mechanism, downgrade operation, and human intervention. If there are no exceptions, the single command ends.

The execution monitoring module needs to track the instruction status in real time, and the design state machine model monitors the transition from “pending execution” to “executing” and then to “completed”. If timeout or error occurs, it will turn to “abnormal”. The state transition function can be formalized as, where S is the state set, f is the state transition function, and CmdExecResult is the execution result.$S\left( {t+1} \right){\text{ }}={\text{ }}f\left( {S\left( t \right),{\text{ }}CmdExecResult} \right)$

Exception handling strategies include retry mechanisms, demotion operations, and manual intervention. For transient errors, automatic retries are allowed N times, with an increasing interval between retries, such as an exponential backoff strategy.$WaitTime=WaitBase*{2^{RetryCount}}$If the retry fails, a demotion policy is triggered to perform alternative actions or reduce the scope of the action. Serious abnormalities will be reported to the monitoring center and operation and maintenance personnel will be notified to intervene.

To sum up, this chapter introduces in detail the key module implementation and algorithm design of the main distribution network integrated network command system, from the bottom foundation of data acquisition and preprocessing, to the in-depth application of intelligent scheduling algorithm, and then to the closed-loop control of command execution and feedback, which provides all-round technical support for the system and ensures the efficiency, security and intelligence of power dispatching.

In the command execution module, a practical example involves the automated issuance of load shedding commands during peak demand periods. The execution monitoring module tracks these commands, transitioning states from “Pending” to “Executing,” with timeouts or errors leading to the “Abnormal” state, formalized as $f\left( {S,\,CmdExecResult} \right)\, = \,S'$, where the transition function f is defined based on execution outcomes.

An assessment of the module’s effectiveness includes measuring the response time to execute commands, the accuracy of state transitions, and the effectiveness of the exception handling strategy. For instance, the module is evaluated on its ability to correctly transition commands within 5 s, accurately detecting and handling exceptions with a success rate above 95%, and effectively recovering from failures using mechanisms like exponential backoff and demotion policies. These assessments validate the system’s capability to ensure reliable command execution and swift issue resolution, thereby reinforcing the system’s overall robustness and efficiency.

Despite the overall design and positive reviews, there are potential areas for improvement in command execution and feedback modules. On the one hand, it combines machine learning prediction model to predict the possibility of command success, so as to optimize retry strategy and enhance the dynamic adaptability of retry mechanism. Another is real-time analytics for improved anomaly detection, using advanced anomaly detection algorithms to identify problems earlier and prevent minor failures from escalating into major outages. The integration of predictive maintenance programs can also predict hardware failures before they affect command execution, further improving system reliability. Finally, exploring the use of distributed ledger technology for command audit trails can improve transparency and facilitate root cause analysis when anomalies occur, adding another layer of resilience to the system.

Experimental evaluation

In order to evaluate the performance of the system, we build a highly simulated test environment. This environment integrates multiple real-world grid models, including complex grid topologies, multi-type power plant models (e.g. thermal, hydropower, wind and solar power plants), and diverse load models (e.g. residential, commercial, industrial loads). In addition, the simulation platform uses software tools such as MATLAB/Simulink, PSS/E or OpenDSS to simulate power system dynamics at different time and space scales to ensure the accuracy and practicability of simulation results.

Experimental design

We elaborate the experimental design from three aspects: power plant model, load model and power network topology.

Power plant model: including thermal power, hydropower and wind power plant three types. The thermal power plant has a capacity of 1000 MW and an efficiency of 35%, the hydropower plant has a capacity of 800 MW and an efficiency of 90%, and the wind power plant has a capacity of 500 MW and an efficiency of 75%.

Load model: Including residential, commercial and industrial three types. Residential load demand is 200 MW, peak hours 18:00 to 22:00; commercial load demand is 300 MW, peak hours 10:00 to 18:00; industrial load demand is 400 MW, peak hours 08:00 to 20:00.

The selection of experiment designs and parameters was grounded in a thorough understanding of real-world power grid dynamics and operational challenges. Power plant models were chosen to reflect a realistic mix of generation capacities and efficiencies prevalent in current grids, with a particular focus on balancing thermal, hydropower, and renewable sources. These choices enable the assessment of system performance under varying energy supply conditions, thereby ensuring the generality and applicability of findings.

The simulation 78leverages industry-standard tools such as MATLAB/Simulink, PSS/E, and OpenDSS. MATLAB/Simulink offers a flexible platform for modeling complex systems and conducting simulations, whereas PSS/E is widely used for power flow and dynamics studies, providing a high level of realism in power system behavior. OpenDSS, a specialized tool for electrical power distribution systems, enhances the study’s scope by allowing detailed analysis of distribution network aspects. The choice of these tools ensures the highest degree of accuracy and realism in simulating the integrated network command system.

To thoroughly assess the effectiveness of the proposed integrated network command system, a meticulous simulation environment was constructed, mirroring real-world power grid complexities. This environment consolidates a variety of grid models, capturing intricate topologies, a diverse mix of generation sources (thermal, hydro, wind, and solar power plants), and a broad spectrum of load profiles (residential, commercial, and industrial).

The simulation leverages advanced software tools such as MATLAB/Simulink, PSS/E, and OpenDSS, ensuring that power system dynamics are meticulously modeled across various temporal and spatial scales. This rigorous approach guarantees that the simulation outcomes offer high accuracy and practical relevance, thereby validating the system’s performance under realistic conditions.

The experimental design is meticulously structured around three core components: power plant models, load models, and power network topologies. Specifically, the power plant models incorporate a thermal power plant with a 1 GW capacity and 35% efficiency, a hydropower plant with an 800 MW capacity and 90% efficiency, and a wind power plant with a 500 MW capacity and a 75% efficiency. These varied efficiency rates reflect the distinct operational characteristics of each power source.

Load models are similarly detailed, reflecting the different consumption patterns across residential, commercial, and industrial sectors. Residential loads peak at 200 MW between 6 PM and 10 PM, aligning with typical evening usage patterns. Commercial loads, peaking at 300 MW from 10 AM to 6 PM, capture the daytime activity in offices and retail sectors. Lastly, industrial loads, topping at 400 MW between 8 AM and 8 PM, mirror the prolonged operational hours of factories and manufacturing units. These detailed profiles allow for a nuanced understanding of how the integrated system manages varying loads throughout the day, thereby testing its adaptability and responsiveness under diverse grid conditions.

Experimental results

This section presents the experimental results of the study, which cover the evaluation of system performance under different conditions, including response time, scheduling accuracy, resource utilization and other key indicators.

Table 1 Performance indicators of basic operation state

Table 1 shows the basic operating state performance indicators, which are key parameters for evaluating the operating efficiency and stability of the grid system. Average response time (0.27 s) represents the average time the system takes to process requests, an important metric for measuring system agility. Dispatching accuracy (98.5%) refers to the accurate execution of grid dispatching instructions, and high accuracy means efficient and reliable grid operation. The resource utilization rate (83.2%) describes the effective use of grid resources, which reflects the economy of the grid. Frequency stability index (± 0.05 Hz) shows the fluctuation range of grid frequency, which is a direct reflection of power system stability.

Table 2 Performance of thermal power model under high load

Table 2 provides performance data for the thermal power model under high loads. Thermal response time increment (+ 0.05 s) indicates the increase in thermal unit response time at high loads. The change of thermal power accuracy (-1.0%) and thermal power utilization (+ 2.0%) reflect the change of dispatching accuracy and resource utilization efficiency of thermal power units under high load conditions respectively.

Table 3 Performance of hydroelectric model under Rapid Response requirements

Table 3 shows the performance of the hydro model under rapid response requirements. Hydroelectric response time (0.22 s) represents the response speed of a hydropower plant to rapidly changing demand. Dispatching flexibility index is an index to measure the dispatching ability of hydropower station when the demand changes. The index value of 9.2 indicates that hydropower station has high flexibility in responding to the demand quickly. The hydropower utilization rate (92.0%) indicates that the hydropower station meets the demand at the same time, and the resource utilization efficiency is very high.

Table 4 Performance of wind and solar power plant model under intermittent energy access

Table 4 describes the performance of the wind and solar power plant model under intermittent energy access. The fluctuation range of wind-solar response time (± 0.03 s) and wind-solar accuracy (± 1.5%) respectively represent the fluctuation of response time and accuracy caused by intermittent resource access of wind-solar power station. The integrated efficiency (78.0%) reflects the efficiency with which PV stations integrate and utilize intermittent energy throughout the grid system.

Table 5 System performance of residential, commercial and industrial load models in peak hours

Table 5 shows system performance during peak hours for different types of loads. For residential, commercial and industrial loads, the average response time increment and the change of dispatching accuracy are given respectively. These data help to understand the impact of different types of loads on grid system performance.

Table 6 Influence of network topology complexity on system performance

Table 6 analyzes the impact of grid topology complexity on system performance. Topological complexity is divided into three levels: high, medium and low, corresponding to average response times of 0.29 s, 0.28 s and 0.27 s respectively. The percentage reduction in resource wastage represents the reduction in resource wastage after simplification of the grid topology. The results show that the lower the topology complexity, the shorter the average response time of the system and the higher the percentage of resource waste reduction, indicating that simplifying the power grid topology is helpful to improve the system performance and resource utilization efficiency.

Table 7 Comparison with traditional command systems

As shown in Table 7, the proposed system exhibits a marked improvement over traditional command systems, offering almost twice the speed in response times, a 3.5% boost in dispatching accuracy, and an 8.2% increase in resource utilization. This stark contrast highlights the enhanced capabilities of the integrated system, attributed to its advanced algorithms and technology.

Table 8 Comparison with state-of-the-art smart grid solutions

As shown in Table 8, in the context of leading smart grid solutions, the proposed system maintains its competitive edge. It achieves the fastest average response time among the compared systems, underlining its responsiveness. Moreover, it boasts a tighter frequency stability index, crucial for grid reliability, and demonstrates a moderate advantage in integration efficiency over Competitor B, while trailing slightly behind in this metric compared to Competitors A and B. The anonymized naming convention preserves confidentiality while allowing for a fair assessment of market positions.

Overall Discussion: Both tables affirm the advanced performance of the proposed integrated network command system against conventional and contemporary benchmarks. The system’s expedited response times, superior dispatch precision, and optimized resource usage set it apart, affirming its potential to elevate the efficiency and reliability of modern power grids. The frequency stability advantage further underscores its capability to handle fluctuations gracefully, a critical attribute in the integration of intermittent renewable energy sources. Despite slight variations in integration efficiency, the holistic performance metrics position the proposed system as a compelling choice for upgrading grid management infrastructure.

Case analysis

This section deeply discusses the application and subsequent optimization effect of the integrated network command system of main distribution network in Incident Response Service of specific regional power grid through an actual case analysis, so as to verify the actual combat capability and optimization potential of the system.

In the summer of 2023, a coastal city encountered rare high temperature weather, which led to a surge in power grid load, especially in the evening, when residents ‘refrigeration load reached a historical peak. At the same time, a sudden typhoon caused an important thermal power station in the region to temporarily reduce production due to fuel transportation interruption, and the contradiction between power supply and demand suddenly became tense. The city’s power grid adopts an integrated network command system for emergency dispatch, which integrates thermal power, hydropower, wind and solar power generation and complex power grid topology, and its performance needs to be verified under extreme conditions.

In the process of implementation, the system first responds to the increasing trend of high temperature load in real time through integrated weather forecasting model, dynamically adjusts power generation plan, increases the output of hydropower and wind power stations, and reserves electricity in advance. At the same time, after typhoon warning, the system responds quickly and dynamically adjusts the scheduling strategy based on DRL algorithm to ensure the power supply of key users first, reduce non-emergency loads and ensure social stability. In addition, in the face of power plant production reduction, the system quickly calculates alternatives, adjusts power flow of the grid, enables backup lines, increases power support in adjacent areas, and at the same time calls energy storage power stations to stabilize load peak-valley differences to ensure that the grid frequency is stable within the allowable range. In the case of typhoon causing partial line damage, the system quickly locates the fault point through intelligent inspection UAV and online monitoring, and the dispatching center immediately starts the emergency plan, quickly isolates the fault section, adjusts the network structure, restores the power supply, and reduces the impact of power failure.

According to the evaluation results, the system showed excellent response ability after receiving the information of power plant reduction, and completed the adjustment of initial dispatching strategy in only 15 min, about 50% faster than the traditional dispatching method. At the same time, intelligent dispatching effectively eased the contradiction between supply and demand, reducing the gap between supply and demand during peak hours from the estimated 200 MW to 50 MW. In addition, the system successfully reduced the power outage time of users by an average of 30% by prioritizing power supply to critical users and rapid fault recovery, thus increasing user satisfaction to 95%. These results demonstrate significant improvements in system responsiveness, load balancing and user satisfaction.

In order to further improve the system performance and meet future challenges, a series of optimization measures and enlightenment have been implemented. First, the system was upgraded, including improving the accuracy of weather prediction models and enhancing the robustness of emergency dispatch algorithms in extreme weather to better predict and respond to adverse weather conditions. Secondly, the investment in intelligent inspection and maintenance technology has been increased, aiming at reducing power outages caused by external factors and ensuring the stable operation of the power grid. Finally, demand-side management is introduced to interact with users through smart meters and flexibly adjust loads, which not only improves the overall response efficiency of the system, but also enhances user participation and satisfaction. These measures improve the intelligent level of power grid, enhance its adaptability to complex situations, and provide valuable experience and enlightenment for future power grid management.

The case study focuses on a scenario where the integrated network command system was deployed to manage a grid facing simultaneous challenges of high temperatures and a power plant outage due to extreme weather. The system’s weather forecasting component accurately predicted the surge in demand, triggering proactive load shedding measures. By integrating real-time weather data, the DQN-based scheduling algorithm dynamically adjusted the power generation mix, prioritizing available renewable resources to mitigate the shortfall, thereby minimizing the need for load shedding.

The case revealed that the system effectively navigated the crisis, averting widespread blackouts. User feedback highlighted improved satisfaction rates due to minimal disruption to services, validating the system’s user-centric design. Performance metrics indicated a swift recovery from the outage, with grid stability restored within minutes, demonstrating the system’s robustness and responsiveness.

In the aftermath, an analysis of system logs and user feedback confirmed the effectiveness of the demand-side management strategies employed. The system’s interactive interface allowed for efficient communication with consumers, facilitating load adjustments during peak periods. The integration of AI-powered predictive analytics not only optimized the response to the immediate crisis but also provided valuable insights for future demand forecasting and grid planning, thereby contributing to a more resilient and adaptable power grid infrastructure.

Conclusion

Through the in-depth analysis of this study, the construction and implementation of the main distribution network integrated command system proves its core value and advanced technology in modern power grid management. The success of the system design lies in the comprehensive consideration of the three requirements of function, performance and safety, realizing real-time data processing, intelligent decision support, rapid fault response and remote monitoring management, providing a solid guarantee for the safe and stable operation of the power grid. Especially in the complex and changeable power grid environment, the system can adapt quickly, such as facing the dual challenges of high temperature load surge and typhoon caused by the power plant production reduction, through the integrated weather forecast, intelligent scheduling algorithm and rapid fault recovery mechanism, the system effectively alleviates the contradiction between supply and demand, reduces the impact of power failure, and significantly improves user satisfaction. At the technical level, intelligent algorithms such as deep Q network (DQN) not only improve the accuracy and efficiency of dispatching strategy, but also enhance the self-learning and self-optimization ability of the system, which is very important for dealing with uncertainty in power grid operation. The application of data fusion and preprocessing technology ensures the accuracy and reliability of decision-making, and the strict implementation of security protection mechanism is the cornerstone of ensuring power grid information security. Experimental evaluation and case analysis data show that the system performs well under different operating conditions, especially in response time, scheduling accuracy, resource utilization and other aspects to meet the design expectations, proving the actual combat effectiveness of the system. However, optimization is endless, and future work should focus on continuously improving the accuracy of forecasting models, enhancing the robustness of algorithms, deepening demand-side management and user interaction, and further exploring the deep integration of new technologies such as artificial intelligence in grid dispatching to cope with more complex and changeable grid challenges and promote the development of power systems in a more efficient, smarter and greener direction. This case not only provides valuable practical experience for power industry, but also provides useful reference for other fields to deal with complex system management problems.

Despite the notable achievements outlined, several avenues for refinement and future development are identified in the integrated network command system for main distribution networks.

Insufficiencies: (1) Enhanced Real-World Integration: While the system demonstrates effectiveness in a simulated environment, there is a need to validate its performance in live, real-world conditions, considering the unpredictable nature of actual power grid operations. (2) Scalability and Flexibility: As power grids continue to expand and incorporate more renewable resources, the system must be further developed to scale seamlessly with growing complexity and accommodate a wider range of energy sources and grid topologies .(3) Advanced Algorithm Optimization: Although the use of Kalman filters and deep Q networks has improved system performance, continuous algorithmic advancements are necessary to enhance prediction accuracy, especially in the face of increasing data volume and variability.

Future Outlook: (1) Integration of Emerging Technologies: Deeper integration of cutting-edge technologies such as artificial intelligence, machine learning, and blockchain could further automate decision-making processes, enhance cybersecurity, and enable more efficient data management and verification. (2) Demand-Side Management and User Interaction: Developing sophisticated demand-side management strategies and fostering enhanced user engagement through interactive platforms will be pivotal to balance supply and demand dynamically and improve customer satisfaction. (3) Predictive Maintenance and Fault Prevention: Incorporating predictive maintenance algorithms to anticipate equipment failures before they occur can significantly reduce downtime and enhance grid reliability.