Integration and Development Path of Smart Grid Technology: Technology-Driven, Policy Framework and Application Challenges

Abstract

As a key enabling technology for energy transition, the smart grid is propelling the global power system to evolve toward greater efficiency, reliability, and sustainability. Based on the three-dimensional analysis framework of “technology–policy–application”, this study systematically sorts out the technical architecture, regional development mode, and typical application scenarios of the smart grid, revealing the multi-dimensional challenges that it faces. By using the methods of literature review, cross-national case comparison, and technology–policy collaborative analysis, the differentiated paths of China, the United States, and Europe in the development of smart grids are compared, aiming to promote the integration and development of smart grid technologies. From a technical perspective, this paper proposes a collaborative framework comprising the perception layer, network layer, and decision-making layer. Additionally, it analyzes the integration pathways of critical technologies, including sensors, communication protocols, and artificial intelligence. At the policy level, by comparing the differentiated characteristics in policy orientation and market mechanisms among China, the United States, and Europe, the complementarity between government-led and market-driven approaches is pointed out. At the application level, this study validates the practical value of smart grids in optimizing energy management, enhancing power supply reliability, and promoting renewable energy consumption through case analyses in urban smart energy systems, rural electrification, and industrial sectors. Further research indicates that insufficient technical standardization, data security risks, and the lack of policy coordination are the core bottlenecks restricting the large-scale development of smart grids. This paper proposes that a new type of intelligent and resilient power system needs to be constructed through technological innovation, policy coordination, and international cooperation, providing theoretical references and practical paths for energy transition.

1. Introduction

In the context of the worldwide drive for sustainable development and the urgent requirement to tackle climate change, energy transition has become one of the primary challenges in modern society [1,2,3,4]. As the hub of energy transmission and distribution, the advancement of the power grid plays a vital role in realizing the efficient application of new energy and the long-term viability of the energy system [5,6]. The rapid development of artificial intelligence technology and hardware computing power, as well as the requirements of the information society for power supply reliability and power quality, have gradually transformed the traditional power grid structure and operation mode towards intelligence, and the smart grid has emerged as “the times” require [7,8]. The smart grid, by integrating advanced technologies and optimizing resource allocation, significantly enhances the operational efficiency, reliability, and flexibility of the power system. At the same time, it effectively promotes the consumption of renewable energy, optimizes energy management, reduces user costs, and provides crucial support for energy transition and sustainable development, becoming the focus of the multinational strategy [9,10,11,12].

In recent years, studies on smart grids have exhibited twofold features of technological convergence and region-specific development patterns [13,14]. From a technical perspective, four major technical frameworks have gradually taken shape: the advanced metering infrastructure, the advanced asset management framework, the advanced transmission operation system, and the advanced distribution operation system. The advanced metering system achieves precise metering and optimizes power dispatching through smart electricity meters and big data analysis [15]; advanced asset management enhances the lifecycle management of power grid assets via intelligent technological approaches, minimizing maintenance expenditures and fault risks [16,17]; advanced transmission operation utilizes technologies such as ultra-high voltage transmission and flexible direct current transmission to reduce losses and enhance transmission stability [18,19]; and advanced distribution operation optimizes distribution network operation with the help of intelligent distribution automation and microgrid technology [20]. These technical systems have realized end-to-end intelligent operation and management across the power generation-to-consumption chain through the deep integration of frontier technologies, including communication, control, energy storage, and artificial intelligence. This integration has significantly enhanced the operational efficiency, reliability, and flexibility of the power grid [20]. Meanwhile, different regions, based on their own energy structures, economic development levels, and policy orientations, present differentiated development paths for smart grids. The United States focuses on user-side interaction and market mechanism innovation, Europe focuses on cross-border power grid interconnection and carbon emission control, while China promotes the “centralized + distributed” collaborative model, relying on the ultra-high voltage backbone grid [21,22,23,24].

However, although the smart grid has achieved remarkable development in terms of technology, application, and policy promotion, its continuous development still faces a series of problems and challenges [25,26,27]. Technical bottlenecks are the key factors restricting the development of smart grids. A series of technical problems, such as the reliability of information and communication systems, the real-time processing capacity of massive data, and the complexity of multi-energy collaborative scheduling, urgently need to be solved. Furthermore, issues such as cyber security risks and the non-uniformity of national policy standards also restrict the large-scale deployment and healthy development of global smart grids [28]. Based on the three-dimensional analysis framework of “technology—policy—application”, through a systematic literature review and comparison of cases from multiple countries, this paper systematically analyzes the technical core architecture, regional development model, and application scenarios of the smart grid, and explores the key challenges and future trends for the sustainable development of the smart grid, aiming to promote technological innovation, policy support, and cross-regional collaboration. The aim is to provide new theoretical perspectives and practical paths for breaking through the industry’s predicament.

2. The Core Architecture of Smart Grid Technology

The technical architecture of smart grids represents a complex, multi-layered system, and at present, there is still a lack of a fully unified classification criterion. The State Grid Corporation of China has put forward that the smart grid architecture is composed of “8 professional branches, 26 technical fields, 92 standard series, and multiple specific standards”. The professional branches and technical fields are illustrated in the figure. The EU M/490 project has proposed a five-layer smart grid architecture model (SGAM), which covers the business layer, functional layer, information layer, communication layer, and component layer. Each layer represents a smart grid plane, spanning the transmission and distribution chains from power generation to users, as well as all levels of information management in the power system from top to bottom. The National Institute of Standards and Technology (NIST) in the United States has put forward a conceptual reference model for smart grids, classifying them into seven domains: users, electricity market, electricity market operations and operators, power supply, operations, transmission, and distribution [29,30,31].

Therefore, the division of the smart grid technology architecture varies among different research institutions and application scenarios, but all revolve around core links such as power generation, transmission, distribution, and consumption. Drawing from research experience and practical applications, the core architecture of smart grid technology can be categorized into the perception layer, network layer, and decision-making layer, based on the functional hierarchy depicted in Figure 1.

2.1. Perception Layer

The perception layer of the smart grid serves as the foundational component for enabling the “intelligence” of the smart grid. Its primary role is to collect and monitor real-time data across all operational stages of the power grid—including generation, transmission, distribution, and consumption—and transmit these data to the upper layers for processing. The monitoring, control, and optimization functions of the perception layer provide essential data support for the smart grid, analogous to the nerve endings of the human body, which are distributed throughout every segment of the smart grid. This layer plays a critical and irreplaceable role in ensuring the safe, efficient, and stable operation of the power grid. The perception layer is composed of various intelligent terminals and sensor networks that can “perceive” the state of the power system [32,33,34]; these devices are distributed in various fields of the power system to realize the monitoring of different objects. Among them, the more core ones are the use of sensors and measurement technologies and smart electricity meters.

2.1.1. Sensors and Measurement Technology

A sensor serves as a detection mechanism that can perceive information related to a measured object and transform it into electrical signals or other necessary forms of information output [35]. In the power industry, sensors designed for monitoring voltage, current, temperature, pressure, displacement, vibration, and light have been deployed to monitor the operational status of power generation equipment, the condition of transmission lines, and the environmental parameters of power transformation and distribution equipment.

At present, sensors are developing towards accuracy, timeliness, and intelligence [36,37,38,39,40]. They can accurately perceive and detect defects early and reasonably judge them, thereby measuring various physical quantities in the power grid system more accurately and achieving more precise control and management [41,42]. In response to the high-frequency and wideband characteristics that are inherent in ultra-high-frequency partial discharge signals, Li et al. (2016) [43] proposed an online monitoring sensor for ultra-high-frequency partial discharge utilizing a level scanning approach, fulfilling the requirements for online monitoring of ultra-high-frequency partial discharge in power transformers. Xu et al. (2025) [44] proposed a current sensor based on the tunneling magnetoresistance (TMR) effect in response to the high-precision and high-resolution requirements of the smart grid for a weak current. The schematic diagram of the sensor structure is illustrated in Figure 2. This sensor boasts advantages such as a broad measurement range, high precision, high resolution, and non-invasive measurement capability, thereby satisfying the smart grid’s requirements for weak current monitoring.

2.1.2. Smart Meter

Smart electricity meters are sophisticated metering devices that gather, analyze, and manage electric energy information data through the integration of modern communication technology, computer technology, and measurement technology [45]. In general, they consist of four key components: a measurement unit, a data processing unit, a communication module, and a user interface [46]. This mechanism can precisely record information such as users’ power consumption, voltage, and frequency, while supporting functionalities including remote meter reading, time-of-use tariff calculation, power consumption control, and two-way interaction, as well as alarm and early warning. As a pivotal element of the perception layer, they perform a crucial function in the power distribution and consumption sectors [47].

With the construction of smart grids and users’ demands for energy management, smart meters are constantly upgrading in technology and highly integrated in functions. By leveraging technologies including big data, artificial intelligence, and machine learning, smart electricity meters will exhibit enhanced intelligence and automation capabilities. Wilcox et al. (2019) [48] combined smart electricity meters with big data platforms, effectively enhancing the intelligent management of data. Liao et al. (2023) [49] combined two long short-term memory networks to develop a multi-energy management system, enabling smart meters to monitor and record residential electricity consumption through machine learning models to predict equipment behavior, thereby enhancing management flexibility. Meanwhile, smart meters will also be capable of automatically identifying and diagnosing faults, achieving automatic alarms and automatic repairs, and enhancing the reliability and stability of the power system. Zhou et al. (2022) [50] proposed a smart meter fault diagnosis model based on the improved Capsule Network (CapsNet), which effectively improved the accuracy of fault diagnosis and shortened the training time. To facilitate installation and use, smart electricity meters will develop in the direction of miniaturization and low cost. By adopting advanced manufacturing techniques and materials, both the size and mass of smart electricity meters can be reduced, while their performance and reliability are concurrently improved. Additionally, with the continuous advancement of technology and the growing fierceness of the market competition, the cost of smart electricity meters is anticipated to decrease progressively.

2.2. Network Layer

The network layer acts as the “neural network” of the smart grid, assuming the critical responsibility of data transmission and device interconnection. Communication technology serves as its core framework [51]. As depicted in Figure 3, an illustrative communication architecture in the smart grid employs the Internet and Internet Service Provider (ISP) as backbone networks to interconnect the distributed subnets [52]. Functioning as a bridge for communication and collaboration across all segments, the network layer is tasked with reliably and efficiently delivering the massive front-end-collected data from the perception layer to the decision-making layer. Concurrently, it promptly disseminates control instructions to on-site devices, ensuring seamless information flow between the two layers. The power communication network has undergone an evolution process from analog dedicated lines and low-speed carriers to digitalization and packetization. In the early days, power grid communication mainly relied on low-speed means such as ultrashort wave radio stations, carrier machines, and power line communication, which could only meet the limited data transmission requirements, such as remote measurement and remote signaling. Since the advent of the 21st Century, the integration of optical fiber and Ethernet technologies into power systems has led to substantial improvements in communication rates and capacities, thereby establishing high-speed channels for diverse business data transmission [53].

The network layer of the smart grid encompasses diverse communication media and protocols, establishing a three-dimensional data transmission network that spans from local- to wide-area scales. Key technologies and components of the network layer include wide-area cellular communication, local wireless communication, wired communication, and power line carrier communication, among others [54]. At the network layer, not only hardware media are required, but also multiple international/industry standard protocols to ensure the compatibility of data formats and interaction rules between devices and systems [55].

2.2.1. Wide-Area Cellular Communication

Wide-area cellular communication is the use of public or dedicated cellular mobile communication networks (2G/3G/4G/5G) to carry power grid data communication. Among them, 4G has been widely applied in scenarios such as distribution automation terminals and power consumption information collection. The latest 5G, due to its advantages such as high bandwidth, high reliability, low latency, and large connection density, is highly expected to be used in the critical communication of power grids [52,56]. At present, power companies in many countries are cooperating with telecommunications operators to carry out 5G+ smart grid pilot projects [57,58], applying 5G to scenarios such as distribution network automation, high-definition video return transmission for unmanned aerial vehicle inspection, and coordinated control of distributed new energy to test its performance.

2.2.2. Local-Area Wireless Communication

Local-area wireless communication is conducted in localized environments such as substations, distribution rooms, and user premises. In smart grid applications, short-range wireless technologies (e.g., WiFi, Bluetooth, ZigBee) and low-power wide-area network (LPWAN) technologies (e.g., LoRa, SigFox) are commonly utilized [59,60]. Communication reliability represents a fundamental requirement for smart grid applications. To address the inherent unreliability of wireless transmission, Mohammadi Nejad et al. (2016) [61] proposed a transmission redundancy-based strategy to enhance the reliability of Neighborhood Area Network (NAN) wireless communication. This approach integrated the communication delay requirements of smart grids as constraint conditions, and its effectiveness was verified through analytical validation.

Additionally, collaborative resource allocation among multiple nodes in local networks is essential to meet the multi-dimensional resource demands of new power services. Aiming at challenges such as limited node resources, inflexible resource allocation, and high complexity in multi-dimensional resource management, Tang et al. (2024) [62] developed a multi-objective joint optimization model for the collaborative allocation of communication, computing, and storage resources in local power wireless communication networks. The simulation results demonstrated that this method can reduce the overall system delay and improve network utilization efficiency. Characterized by low deployment costs and low power consumption, these non-cellular wireless technologies serve as suitable supplements to cellular networks.

2.2.3. Wired Communication and Backbone Network

The power system has traditionally relied heavily on wired communication, typically using optical fibers, coaxial cables, microwaves, etc. at the backbone level to carry critical services. Power grid enterprises have universally established their own extensive optical fiber communication networks. By laying optical fiber composite ground wires (OPGWs) on high-voltage transmission lines, these enterprises connect various high-voltage substations and dispatching centers, thereby forming a backbone power optical fiber network. The backbone optical fiber network provides a channel with large bandwidth, low latency, and high security, which is used to carry critical services such as dispatching data and protection signals. It can operate autonomously from the public network, thereby ensuring compliance with the dedicated network isolation and high-reliability requirements for power grid operations.

2.2.4. Power Line Carrier Communication

Power line carrier communication (PLC) technology transmits data by superimposing high-frequency signals on existing power cables without the need for additional communication lines. It is one of the commonly used communication methods on the distribution and consumption sides [63]. The advantage of PLC lies in achieving communication relying on the existing power grid. However, due to the physical characteristics of the power lines themselves, its reliability and rate are vulnerable to power grid interference. Therefore, it is usually used as a backup or low-cost supplementary means [64].

2.2.5. Communication Protocols and Standards

Power grid communication protocols and standards are the key cornerstones for the stable and efficient operation of the power system, ensuring seamless connection in all links from power production and transmission, to distribution and consumption. At the same time, they provide a solid guarantee for the interoperability and system compatibility of equipment from different manufacturers, and are the core support for achieving power grid intelligence and promoting the digital transformation of the power industry. The smart grid adopts a variety of international/industry standard protocols. The IEC 61850 standard for the communication of internal equipment in substations enables plug-and-play interoperability of protection and measurement and control devices [65]; Power dispatching automation extensively employs SCADA protocols like IEC 60870-5-104 and DNP3 to facilitate stable communication between telecontrol terminals and control centers [66,67]. In the realm of time synchronization, to underpin wide-area measurement and protection, the IEEE 1588 Precision Time Protocol (PTP) and satellite timing are prevalently utilized. These technologies ensure the consistency of data collection timestamps across devices, thereby enabling synchronous analysis and control of the entire network [68]. With the proliferation of IP technology, traditional proprietary protocols are progressively transitioning to TCP/IP-based general-purpose protocols, enabling the integration of power communication networks with IT networks.

2.3. Decision-Making Layer

The decision-making layer acts as the intelligent core and command center of the smart grid. By applying sophisticated computational and algorithmic methodologies, it assesses and analyzes the operational dynamics of the power grid to generate timely and optimized control decisions. Its core functionalities include monitoring and forecasting, optimization and dispatch, control execution, and information integration support. The intelligent analysis conducted by the decision-making layer is indispensable in maintaining grid stability across varying operational scenarios, while simultaneously achieving maximum efficiency and cost minimization to the fullest extent.

In conventional power systems, control decisions primarily depend on dispatchers’ experience and centralized control systems such as EMS/DMS. In the smart grid era, the decision-making layer has integrated innovative technologies like big data processing and artificial intelligence, providing automated and intelligent decision-making support for power grids. The information technologies utilized at the decision-making layer are highly diverse. Among them, several key and representative technologies currently include cloud computing and big data platforms, artificial intelligence (AI), distributed intelligent edge computing, and digital twin-based simulation decision-making.

2.3.1. Cloud Computing and Big Data Platform

The data volume in smart grids is experiencing exponential growth, with traditional centralized computing struggling to meet storage and processing demands [69]. Cloud computing offers robust support for power big data via elastic computing and storage resources. In response to the massive data monitored from smart meters, Munshi et al. (2017) [70] proposed a big data framework for smart grid analysis, as illustrated in Figure 4. The framework covers the lifecycle of smart grid data from data generation to data analytics. They presented the framework’s cloud-based platform perspective and validated its feasibility in smart grid data analysis through two use cases [70]. The big data platform based on cloud computing can integrate data from all the links of power generation, transmission, distribution, and consumption; achieve data storage, mining, and sharing; and provide unified data services for upper-layer applications. In addition, big data analysis technology can be used to identify the operation patterns and implicit rules of power grids. Typical applications encompass user electricity consumption behavior analysis, equipment full-life-cycle management, and power market transaction analysis. Through big data mining, actionable decision-making insights can be extracted from complex and diverse datasets, thereby enhancing the scientific validity of decision-making processes.

2.3.2. Artificial Intelligence Technology

Artificial intelligence technology has injected new vitality and wisdom into the smart grid. Machine learning and deep learning algorithms are capable of training and learning from the massive data generated during power grid operations, extracting complex correlations therefrom to guide control decisions [71,72,73].

To address the issue of power theft, Mohammad et al. (2023) [74] proposed an ensemble learning-based information security decision support system for smart grids. It was evaluated using the energy theft detection dataset (ETD2022), which proved that the accuracy of the results of the machine learning method was relatively high. Arcas et al. (2024) [75] aimed at the decision-making problems in the huge data search space and limited time of the power grid, using the whale optimization algorithm to determine and select the best edge nodes for performing service computing tasks. The results show that the method can maintain faster optimization decisions. Jasmine et al. (2025) [76] proposed a hybrid artificial neural network: the Firefly optimization model using deep learning technology to address the complex interaction between energy consumption and external factors. The model uses data from the Tamil Nadu Energy Grid in 2013 to provide predictive insights for strategic grid improvements to utility companies.

The introduction of artificial intelligence has shifted power grid decision-making from being based on experience and rules to being data-driven and self-learning, enhancing the ability to deal with complex scenarios.

2.3.3. Edge Computing and Distributed Intelligence

Edge computing represents a recently emerging computing paradigm. It entails decentralizing computational capabilities to the edge nodes near field devices, thereby reducing central computing burdens and fulfilling real-time processing requirements [77,78]. In the smart grid, edge computing realizes the decision-making architecture of cloud-edge-terminal collaboration by deploying computing units at substations, primary and secondary integration terminals, and microgrid controllers [79,80]. Guerrero et al. (2023) [81] addressed the issue that fragmented information data have a significant impact on the effective application of analytical methods. They described a general distributed analytical platform based on edge computing and computational intelligence. Using a distributed analytical engine, they evaluated the proposed solutions via genetic algorithms, genetic algorithms with evolutionary control, and particle swarm optimization algorithms. Through a case study on calculating smart grid key performance indicators (KPIs), the proposed method demonstrated a superior performance compared with traditional approaches.

2.3.4. Digital Twin and Simulation Decision-Making

Digital twin technology is a new concept that has received high attention in the energy field in recent years [82,83,84]. By constructing a digital mirror corresponding to the physical power grid, it realizes the virtual–real synchronous mapping and simulation of the power grid operation [85,86]. At the decision-making level, the introduction of digital twins can be used to assist in complex decision-making and drills under extreme working conditions. Ozkan et al. (2025) [87] proposed a digital twin model based on the system development life cycle. Figure 5 shows the architecture of the digital twin model, aiming to provide a standardized and structured framework for digital twin development. The schematic diagram delineates a multi-layered system architecture designed for efficient management and operation. At the base lies the “Observable Physical Components,” which are responsible for data collection and transmission to the “Data Collection and Device Control Layer.” This layer then relays the data to the “Digital Twin (DT) Layer,” which functions as a real-time mirror of the physical system, processing and feeding back information. The “Cloud Management Layer” oversees the deployment and management of DT components within the cloud environment, while the “DT Management Layer” focuses on the core operations of the DT. The topmost “User Interface Layer” provides interaction points with both architectural elements, including interfaces for cloud and DT management. Additionally, elements such as the “Design Interface,” “Templates,” “Images,” and “Environments” are part of the cloud management layer, whereas functions like “Configuration,” “Simulation,” “Data Analytics and Reporting,” and “Forecasting” are integral to the DT management layer. The system ensures tight collaboration and information synchronization among the layers through bidirectional data and feedback flows. In addition, they integrate open-source predictive tools and data analysis methods into digital twins to predict future trends and analyze performance. This approach has successfully addressed key challenges related to cost, scalability, integration, regulatory compliance, and standardization, laying the foundation for the advancement of digital twin technology in various fields.

3. Comparison of Smart Grid Development Models

The development models of smart grids vary in different countries and regions, depending on their respective technological foundations, power systems, policy orientations, and market environments. In major economies including China, the United States, and Europe, the developmental trajectories of smart grids exhibit substantial disparities.

Based on the annual report of the State Grid Corporation of China the United States Energy Information Administration, the report of the Eurostat, and industry research, we have compiled the key quantitative data and indicators for the development of smart grids, as shown in

Table 1. Summary table of key quantitative data and indicators for smart grid development.

Dimension	China	United States	Europe	Reference/Source
Technical Scale	① UHV transmission lines: 54,000 km by 2024, accounting for over 75% of the global total ② Smart meter penetration: 92% in 2024, covering over 560 million users ③ Renewable energy grid integration: 1.1 billion kW (wind + PV) by 2023	① Distributed energy capacity: 350 GW in 2023, accounting for 28% of the total installed capacity ② 5G grid applications: Over 200 pilot projects in 2024, covering distribution automation ③ Smart meter coverage: 85% in 2023, serving 120 million users	① Cross-border transmission: 18% of the total electricity generated traded across borders in 2024 ② Virtual power plants: Over 500 in 2023, with an aggregated capacity of 40 GW ③ Renewable energy absorption rate: 92% in 2024	① China: 2023–2024 Electric Power Industry Statistical Report, National Energy Administration (NEA); Construction data for the year 2024, State Grid Corporation of China (SGCC) ② United States: 2023 Annual Electricity Market Report, Energy Information Administration (EIA); 2024 Annual Statistical Report on Technological Applications, Electric Power Research Institute (EPRI) ③ Europe: 2024 Market Transparency Report, European Network of Transmission System Operators for Electricity (ENTSO-E); Renewable Energy Statistics for 2024, Eurostat
Application Impact	① Demand response potential: 80 million kW peak load reduction capacity in 2023 ② Line loss rate: 1.2 percentage points lower than 2018, saving over 30 billion kWh annually ③ Rural electrification: Smart microgrids covering 100,000+ villages by 2024	① User cost savings: 30% annual electricity bill reduction for households in the Pecan Street project ② Outage recovery time: 60% shorter in smart grid areas vs. traditional grids ③ Carbon reduction: ~120 million tons CO2 avoided via smart grid technologies in 2023	① Industrial energy optimization: 18% annual energy savings and 3000 tons CO2 reduction at Schneider’s Spain plant ② EV-grid integration: Over 5 million users in 2024 ③ Peak–valley difference: 8 percentage points lower than 2018	① China: Demand Response 2024 Annual Report, China Electricity Council (CEC); Line Loss Management Bulletin, State Grid Corporation of China (SGCC) ② United States: Pecan Street Official Project Evaluation Report; Power grid reliability analysis, North American Electric Reliability Council (NERC) ③ Europe: Schneider Electric’s official website project report; Joint Report of the Electricity Industry, Associationdes Constructeurs Europeensd’ Automobiles (ACEA)
Market & Investment	① Annual smart grid investment: 600 billion yuan (~82.8 billion USD) in 2024 ② Energy storage capacity: 45 GW in 2023, accounting for 35% of the global total ③ Electricity market turnover: Spot market trading exceeded 1.2 trillion kWh in 2023	① Private investment share: 70% of 2023 smart grid investment from enterprises/capital ② Demand response market size: 12 billion USD in 2023 ③ Microgrid market: Over 500 industrial microgrid projects in 2024, with investment exceeding 8 billion USD	① EU grid upgrade investment: Over 350 billion EUR (~411.64 billion USD) cumulatively (2021–2024) ② Renewable energy subsidies: ~40 billion (~47.04 billion USD) EUR for grid adaptation in 2023 ③ Retail market liberalization: 90% of users able to choose suppliers in 2024	① China: 2024 Energy Investment Statistics, National Energy Administration (NEA); Annual Report of China Electricity Trading Center ② United States: Smart Grid Investment Trend Report, North American Electric Reliability Council (NERC) ③ Europe: Energy Investment Bulletin, European Commission; Report on Market Liberalization, EU Agency for the Cooperation of Energy Regulators (ACER)

. The indicators focus on technical scale (coverage, installed capacity), actual impact (energy conservation, emission reduction, cost), and market vitality (investment, transaction scale), highlighting the scale effect and significance of the smart grid. Afterwards, based on quantitative data, we will specifically analyze the differences in the development models of smart grids among China, the United States, and Europe.

3.1. Differences in Regional Development Paths

China’s smart grid development adheres to the principle of “unified planning, independent innovation, policy guidance, and market-driven promotion”. Under the impetus of strong national policies, major power enterprises such as the State Grid Corporation of China and China Southern Power Grid Company Limited serve as the implementing entities. Aligned with the national energy development strategy and power grid upgrading needs, it focuses on intensive research and large-scale construction in key domains such as ultra-high voltage transmission technology [88], grid dispatching automation, and smart substations [89].

The development of the United States smart grid exhibits the characteristics of “technology leadership, market-driven dynamics, multi-stakeholder participation, and innovation prioritization”. Its development model emphasizes intelligent transformation on the distribution and consumption sides to address the requirements of distributed energy integration, demand-side management, and enhancement of power supply service quality for users [90,91]. As a country with highly developed information technology and communication technology, the United States has fully integrated its technological advantages into the construction of smart grids, actively explored the application of new technologies, such as smart meters and distributed energy management systems, and formed diversified investment and operation models; a pattern involving multiple parties such as power enterprises, energy service companies, and technology enterprises [92].

The development of the smart grid in Europe is guided by the path of “absorbing renewable energy, integrating distributed generation, interconnected power supply, and emphasizing coordination”. European governments highly value the development and utilization of renewable energy and consider smart grids as a crucial means to facilitate the grid—connection and consumption of renewable energy. The construction of smart grids in Europe emphasizes the flexible access of distributed generation and microgrids, as well as the interconnection and power exchange among power grids of various countries, creating a power grid system with high flexibility and adaptability to adapt to the operational challenges of the power system brought about by the high proportion of renewable energy access [93]. Some cities in Europe have built virtual power plants, aggregating numerous distributed power sources and adjustable loads to participate in electricity market transactions, complementing the traditional centralized power dispatching model. In technical implementation, Europe places significant emphasis on standardization and interoperability [94,95]. It ensures compatibility among equipment from diverse manufacturers via international standards, thereby establishing a foundation for large-scale cross-border power grid collaboration. The super smart grid refers to a power grid system that integrates artificial intelligence and cross-regional energy interconnection on the basis of a smart grid to achieve full-chain autonomous optimization. At present, European countries are gradually transitioning from smart grids to super smart grids, leveraging emerging technologies to enhance the interconnection of smart grids across Europe.

To sum up, various regions have formed a pattern of “Chinese characteristics and differences from Europe and America” in the development of smart grids. China is dominated by the government and state-owned power grid companies, tending towards large-scale power transmission and overall grid-wide coordination. Guided by the federal government and jointly propelled by states and market entities, the United States prioritizes power distribution optimization and operational flexibility. In Europe, coordinated by the European Union and executed by member states, green transformation serves as the core driver for digital power grid innovation. These divergences mirror the unique characteristics of the respective energy systems. In the future, with advancements in technologies like AI and energy storage, the gaps among the three in technological integration and market synergy may gradually diminish, though their development trajectories will likely retain regional distinctiveness.

3.2. Policy-Driven and Market Mechanism

The development of smart grids has been decisively influenced by policy support and a market mechanism design. Governments of all countries provide impetus for the construction of smart grids through policy frameworks, but there are differences in the implementation focus and marketization degree [96].

In terms of policy-driven development, the Chinese government has pointed out the direction and provided guarantees for the construction of smart grids by formulating a series of medium- and long-term development plans, industrial policies, and technical standards. As early as around 2010, the State Grid Corporation of China launched the “Strong and Smart Grid” program, dividing 2009–2020 into three phases: planning and pilot, comprehensive construction, and leading and upgrading. It has been steadily promoting the transformation of smart power transmission and distribution facilities and the integration of information and communication. Recently, the state has issued the “Action Plan for Accelerating the Construction of a New Power System (2024–2027)”, clearly defining tasks such as shared energy storage and microgrid dispatching. At the same time, it will focus on investment guidance. By 2024, the State Grid of China will increase its investment in ultra-high voltage, digital upgrading and other fields to 600 billion yuan (~82.8 billion USD). In contrast, the United States relies more on state policies and regulatory incentives for continuous driving [92]. For instance, California requires that 60% of its electricity comes from renewable energy sources by 2030, supports the deployment of energy storage through subsidies, and allows smart grid investments to be included in electricity price recovery. The European Union adopts a multi-level policy system [93]. On the one hand, it sets common goals for member states through EU directives and action plans, such as requiring the opening of the electricity market, ensuring consumer participation, and data privacy; on the other hand, it provides research and development and demonstration funds (such as the “Horizon 2020” scientific research project, the “Innovation Fund”, etc.) to support the pilot of new technologies. Drawing insights from China’s State Grid planning, the U.S. Department of Energy’s report, and the EU Grid Action Plan, Figure 6 illustrates the smart grid investments by China, the United States, and the European nations over the past five years. These countries have allocated funds to sectors including ultra-high voltage/flexible direct current (UHV/FDC) transmission, smart meters, grid digitalization, energy storage technology, and new energy grid integration. Notably, UHV/FDC transmission and new energy grid integration have attracted relatively substantial investments.

Regarding market mechanisms, the United States and Europe have established relatively mature electricity market frameworks, facilitating the adoption of smart grid-related technologies via market-oriented approaches. In contrast, China’s market mechanism remains in the early developmental and pilot phases, primarily driven by regulatory guidance. Since the 1990s, the United States has operationalized electricity wholesale markets and independent dispatching in certain regions. These markets have provided participation space for demand response, energy storage, etc. Meanwhile, the wholesale market and capacity market in the United States encourage industrial and commercial users to reduce loads during peak hours through economic signals, providing auxiliary services for the power grid. Most countries in Europe have opened up retail and wholesale markets under the unified framework of the European Union. Under the operation of independent transmission operators and distribution companies, ancillary service markets and cross-regional spot markets have gradually formed, giving distributed energy and adjustable loads the opportunity to be compensated [97]. In contrast, China’s power marketization reform is still advancing. In recent years, China has begun to pilot spot electricity markets and regional electricity trading centers, introducing market bidding mechanisms to promote the optimization of electric energy resources. In terms of demand-side management, since the “13th Five-Year Plan”, many provinces in China have carried out demand response and virtual power plant pilot projects. Through a combination of government subsidies and market settlement for ancillary services, they have initially explored the mechanism for user-side resources to participate in peak shaving and frequency regulation. Overall, however, the operation and dispatching of power grids in China are still mainly planned and directive, and the degree of marketization is relatively limited [98].

Based on the above comparative analysis, we have compiled a comparison table of the key dimensions of smart grids in China, the United States, and Europe, as shown in

Table 2. Comparison table of key dimensions of smart grids in China, the United States, and Europe.

Dimension	China	United States	Europe
Technical focus	Ultra-high voltage, centralized dispatching, and intelligent substations	User-side interaction, distributed energy management, 5G applications	Cross-border interconnection, virtual power plants, carbon footprint monitoring
Policy-driven	Government-led, five-year plan-driven, and unified technical standards	Market-driven, state-level autonomous pilot projects, and federal coordinated incentives	Under the framework of the European Union, multiple countries work in coordination, with green transformation taking priority
Market mechanism	The market-oriented pilot stage is led by state-owned enterprises	Mature wholesale/retail markets, with multi-party participation	Open up the retail market and conduct cross-regional spot trading
Main disadvantages	Low degree of marketization	Cross-state coordination is complex and the cost of renovating old power grids is high	Cross-border policy differences and responses to the volatility of renewable energy

. It is worth noting that government regulation remains pivotal in smart grid promotion across all market models. The mechanism design integrating policy guidance and market forces serves as a cornerstone for large-scale smart grid deployment. Policies offer directional guidance and initial momentum, whereas market mechanisms enhance resource allocation efficiency and innovation vitality during the mature phase. For China, how to draw on international experience to improve the market mechanism and encourage more social capital to participate in the future will be a key direction in continuously promoting the construction of smart grids. For European countries and the United States, how to solve market failures (such as long-term insufficient investment, insufficient upgrading of rural areas and weak links, etc.) through policy guidance is also an important challenge.

4. Typical Application Scenarios of Smart Grids

The value of the smart grid lies in its extensive application scenarios, covering various fields from cities to rural areas, and from households to industries. The following will respectively discuss the typical application practices of smart grids in urban smart energy systems, rural electrification, and industrial fields, and analyze the key technologies and policy challenges that they face.

Table 3. Summary of typical smart grid application cases.

Application Scenarios	Case Name	Core Technology	Major Achievement
Urban smart energy system	Pecan Street project (U.S.)	Smart meters, energy storage	Peak–valley reduction, cost saving for users
Rural electrification	Rural distributed photovoltaic power station	Distributed photovoltaic power station, smart microgrids	High electrification rate, clean energy adoption
Industrial sector	Schneider Electric Microgrid (Spain)	Industrial microgrid, energy management system	Energy cost reduction, grid-independent operation

shows typical cases, core technologies, and major achievements covering the urban, rural, and industrial sectors. We conducted a systematic and detailed analysis of these typical cases.

4.1. Urban Smart Energy System

The urban smart energy system denotes the integrated governance of energy carriers like electricity, heat, and gas within urban areas through the application of digital technologies, aiming to achieve an efficient, clean, and reliable energy supply. Smart grids enhance the flexibility and intelligence of urban energy systems via distribution automation, distributed energy integration, energy storage deployment, and demand-side management [99,100,101,102].

The Pecan Street project, implemented in Austin, TX, USA, has established a smart energy demonstration at the urban community level. In the Mueller community of Austin, this initiative deployed real-time energy consumption monitoring systems and distributed energy facilities—comprising smart meters, rooftop photovoltaic systems, home electric vehicle charging stations, and household batteries—across 1000 residential households, 25 small commercial users, and 3 educational institutions. Through an online platform, users can access real-time power consumption data, set energy usage budgets, and utilize intelligent software to manage the electricity consumption of various home appliances. When there is an excess of photovoltaic power generation, residents can also sell the surplus electricity through the power grid. The Pecan Street project has proved that the microgrid integrating distributed generation + energy storage + smart power consumption at the urban community level can achieve peak–valley reduction and energy self-sufficiency, saving users’ costs while providing auxiliary support for the large power grid. In addition, this project has accumulated detailed big data on electricity consumption, providing valuable materials for the study of urban energy consumption patterns.

The development of urban smart energy systems highlights the significant potential of smart grids in improving energy efficiency, optimizing the supply–demand balance, and advancing clean energy adoption. Whether it is a community-level microgrid or a city-level intelligent regulation and control platform, these practices are all exploring new models of multi-energy collaboration and supply–demand interaction, enabling urban energy to shift from a one-way supply to two-way interaction and intelligent optimization.

4.2. Rural Electrification

In vast rural areas, smart grid technology is used to enhance the penetration rate of electricity and the quality of power supply services. Especially in remote and underdeveloped areas, smart microgrids and a distributed energy supply have become important means to achieve rural electrification [103,104].

By deploying small-scale, intelligent, and self-sustaining power systems, many areas that were previously unable to access the large power grid have used electric lights and electrical appliances for the first time. This not only improves the quality of life, but also brings new opportunities for social and economic development. For countries that have achieved full electrification, the application of smart grid technology in rural areas is more reflected in improving the power supply quality and promoting the utilization of clean energy. As early as 2015, China announced that it had achieved a 100% rural electricity access rate. A new round of rural power grid renovation and upgrading has improved the power supply reliability and voltage quality in remote rural regions. Currently, a significant number of distributed photovoltaic power stations in China’s rural areas have been integrated into local power grids. In specific regions, the electrification of agricultural production activities, such as electric irrigation and grain drying electrification, has also been promoted. The application of smart grid technologies will continue to boost the management efficiency and service standards of these systems.

4.3. Industrial Sector

The industrial sector serves not only as a primary electricity consumer but also as a critical application scenario for smart grid technologies. Through the smart grid, the precise management of industrial electricity consumption, energy efficiency improvement, and interactive regulation with the power grid can be achieved. Typical applications include the deployment of factory microgrids and energy management systems, etc. [105].

In certain large industrial parks, enterprises deploy proprietary distributed energy resources, such as rooftop photovoltaics, power plant waste heat power generation systems, and energy storage systems, and integrate them with intelligent energy management to achieve partial energy self-sufficiency and optimization. For instance, Schneider Electric’s facility in Spain’s Navarra region has established the country’s first industrial microgrid [106]. By integrating photovoltaics, bicycle-powered energy storage, and energy management systems, the microgrid enables optimal energy dispatch and digital management. It can operate autonomously during grid outages and, under normal conditions, intelligently switches operational modes based on electricity prices and load demands to meet the factory’s energy needs at a minimum cost.

In California’s tech parks, large-scale lithium battery energy storage systems and gas trigeneration units have been implemented. These systems operate in grid-connected mode to sell electricity during normal periods and disconnect from the grid during disasters to ensure a continuous energy supply for the parks. The energy internet refers to an integrated energy system that combines multiple energy networks such as electricity, heat, and gas, and realizes collaborative dispatching through digital technology. In China, industrial parks in Zhejiang, Jiangsu, and other provinces have initiated “energy internet” demonstration projects that integrate distributed photovoltaics, energy storage, electric vehicle charging piles, and adjustable loads of industrial enterprises, with unified dispatch managed through energy management platforms. Some industrial enterprises’ captive power plants are also connected to park microgrids, allowing excess electricity to be consumed within the park or fed into the grid. This approach not only enhances energy utilization efficiency but also alleviates pressure on the public power grid during peak electricity demand periods.

Representative practical cases show that both traditional heavy industries and emerging high-tech industries can achieve a win–win situation with the help of smart grid technology, reducing the energy costs of enterprises while enhancing the overall flexibility and reliability of the power grid.

5. Key Challenges

While smart grids have demonstrated broad application prospects across diverse sectors, their large-scale development still presents numerous challenges. These challenges can be primarily categorized into two interrelated dimensions: technical and policy levels, which often require simultaneous resolutions due to their interdependencies.

5.1. Technical Challenges

The smart grid works in coordination at the perception, network, and decision-making levels, forming a complete technical architecture system. However, it still faces many problems and challenges at the technical level. Based on actual typical application scenarios, the key technical challenges requiring urgent resolution in the future have been systematically analyzed and prioritized.

• Power electronic equipment and grid stability. Smart grids rely on high-precision sensors, but high temperatures, high humidity, electromagnetic interference, and other factors can affect the normal operation of equipment and even shorten its service life. Furthermore, the typical topological structure of the future power grid remains unclear, which brings a certain degree of uncertainty to the construction of infrastructure, such as smart meters. This makes it difficult to precisely plan the deployment methods and scales of equipment, thereby affecting the operational stability of the power grid.
• Information and communication technology challenges. The smart grid involves numerous links and devices, and requires unified communication standards and protocols to ensure that all parts can effectively exchange data and work collaboratively. However, there are still deficiencies in the standardization work in this field at present, especially in the parts related to distributed power sources and energy storage. The communication interfaces and protocols of devices/systems from different manufacturers exhibit inconsistencies. Owing to the absence of unified standards, equipment and technologies across manufacturers vary significantly, leading to suboptimal compatibility and interoperability among smart grid devices and systems. This predicament hinders seamless collaborative operations, exacerbates system integration complexities and costs, and poses obstacles to the large-scale advancement of smart grids [107].
• Challenges in data management and artificial intelligence applications. Data security and privacy protection stand as pivotal challenges in smart grid operations. As the grid’s informatization and intelligence deepen, the severity of cyber threats grows correspondingly. Relying on extensive real-time data transmission, the smart grid faces risks: leaks of data containing user privacy or grid security information could violate individual rights and even jeopardize grid stability and national security. While existing data security technologies offer basic protections, they remain inadequate for the grid’s dynamic and complex threat landscape. Gaps persist in intrusion detection, network security protocols, data encryption mechanisms, access control systems, and security auditing procedures, which struggle to satisfy the grid’s rigorous security mandates. In addition, the smart grid generates terabytes of data per second, presenting significant hurdles in storage, computation, and data integrity. Issues like signal noise and communication packet loss can induce data corruption or loss, undermining the accuracy of intelligent decision-making. For example, flawed data in load forecasting might lead to suboptimal energy distribution, while errors in fault detection could delay critical maintenance responses. Artificial intelligence (AI) technology has great potential in load forecasting, fault diagnosis, and optimal dispatching, etc.; however, the electricity consumption patterns vary greatly in different regions, and a single AI model may not be applicable. Meanwhile, complex AI algorithms (such as deep reinforcement learning) are time-consuming in their calculation and it is difficult to ensure they meet the requirements of millisecond-level power grid control.

These challenges—spanning security vulnerabilities, data management bottlenecks, and AI scalability—highlight the need for holistic solutions. Future developments should prioritize standardized security frameworks, edge computing for localized data processing, and adaptive AI architectures capable of learning from regional disparities. Addressing these issues is indispensable to realizing the smart grid’s full potential while ensuring its resilience and operational excellence.

The optimistic aspect is that although there is no overall solution at present, many scholars have respectively proposed solutions from aspects such as the stability of smart grid hardware [108,109,110,111,112,113,114,115,116,117,118], communication technology [119,120,121,122,123,124,125,126], and data security [127,128,129,130] to address these challenges. Furthermore, in response to the current problems of the smart grid, various algorithms are also undergoing continuous iterations and optimizations [131,132,133,134,135,136]. To detect network attacks in the smart grid, Masaud et al. (2025) [137] combined deep learning (DL) technology with the whale optimization (WOA) and fish-eel optimization (FMO) algorithms, proposing a new method for detecting network attacks in the SG and forming the WOA–FMO hybrid algorithm, providing a powerful solution for the resilience of the smart grid against advanced network attacks. Kong et al. (2025) [138] addressed the problem of optimizing energy management in smart homes using the improved sparrow search optimization algorithm, improving the stability and cost efficiency of the power grid.

5.2. Policy and Regulatory Challenges

Beyond the technical hurdles, the advancement and large-scale deployment of smart grids also hinge on a robust policy and regulatory framework. Through an analysis of global smart grid development paradigms, the primary challenges in the current policy landscape have been systematically identified.

• The global policy regulatory framework is not coordinated. The smart grid development policies across nations exhibit notable disparities, particularly in technological R&D priorities and capital investment focal points. The absence of dedicated international regulatory bodies has led to a lack of unified, coordinated global strategies for smart grid advancement, thereby hindering the formation of effective synergies. For example, certain developed countries prioritize smart grid upgrading and optimization, whereas some developing nations place a greater emphasis on infrastructure construction and dissemination.
• Funds and business models. The transformation of smart grids usually requires huge investment, with a long payback period and high uncertainty. Traditional grid investment is usually recovered by monopolistic public utilities through electricity charges, but it is difficult to monetize some of the benefits of smart grids (such as reduced power outage losses and environmental benefits) directly. The construction of smart grids requires financial support from multiple aspects, such as governments of various countries, private enterprises, and international organizations. However, due to the different interests and priorities of all parties, there are certain difficulties in the allocation and coordination of funds.
• Public acceptance and coordinated participation. The construction of smart grids sometimes encounters problems of public perception and acceptance. For instance, the construction of new transmission lines and substations in Europe and the United States is often delayed due to community opposition, and the promotion of smart meters in some countries has also slowed down because users are concerned about privacy and radiation issues. Therefore, public non-acceptance of new power grid projects is one of the main obstacles. Public communication and interest coordination need to be strengthened at the policy level. On the one hand, transparency should be enhanced to explain to the public the reliability and environmental benefits brought by the smart grid, as well as the measures to ensure privacy and security. On the other hand, users’ participation and sense of gain can be enhanced through certain compensations or incentives (such as subsidies for users participating in demand response). In addition, the promotion of smart grids also involves multi-departmental collaboration. For instance, the energy department should cooperate with the communication and information industry departments to formulate standards, and the power regulatory authority should work in coordination with the cybersecurity regulatory authority regarding management. This kind of cross-departmental coordination also brings difficulties in the implementation of policies and requires a higher-level overall planning mechanism.

The challenges faced by smart grids are comprehensive and complex. They require not only technological innovation, but also policy innovation and concept renewal. Technical challenges require continuous investment in research and development and for pilot demonstrations to be gradually resolved. However, policy and regulatory obstacles require decision-makers to plan ahead and actively reform the power system and market design. Compared with technical challenges, policy and regulatory challenges currently do not have a clearly unified solution. The policies, regulations, and functional responsibilities vary from country to country and region to region, and they also differ in relation to the construction of smart grids and public opinions [139,140,141]. Therefore, policy and regulatory challenges are more difficult than technological challenges and require global joint efforts and construction.

6. Conclusions

The development of the smart grid has moved from the stage of technological exploration to the stage of large-scale application. The maturity of its technical architecture and the improvement of its policy framework are reshaping the operational paradigm of the energy system. Research shows that the coordinated evolution of the ubiquitous perception layer, the high-speed network layer, and the intelligent decision-making layer has significantly enhanced the flexibility and resilience of the power system. In terms of regional development models, China relies on the ultra-high voltage backbone network and centralized policy promotion, the United States focuses on market-oriented innovation on the distribution side, and Europe emphasizes cross-border interconnection and the integration of renewable energy. The differences in the paths of the three reflect the profound influence of the energy system and technological foundation. However, the standardization difficulties caused by technological heterogeneity, the risks of data security, and privacy protection, as well as the constraints on global collaboration imposed by fragmented policies, remain the key challenges for the in-depth development of smart grids. Future research needs to focus on three aspects: firstly, promote the unified standards of communication protocols and data interfaces to enhance the interoperability of multi-source devices; secondly, develop highly robust artificial intelligence algorithms and edge computing architectures to meet the real-time processing requirements of massive data; and thirdly, establish a cross-border policy coordination mechanism to balance technological sovereignty and open cooperation. With the deep integration of technologies such as 5G, digital twins, and blockchain, the smart grid will accelerate its evolution towards “autonomy” and “decentralization”, and become the core infrastructure for achieving the carbon neutrality goal. This research provides a systematic analytical framework for solving the coupling problem of technology, policy, and application. Subsequently, the research on business models and public participation mechanisms can be deepened in combination with specific regional cases to promote the inclusive development of smart grids.