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Smart-grid optimization increasingly integrates digital twins, reinforcement learning, and cyber-physical resilience analytics into a shared research space. This convergence reflects a shift from isolated grid-control studies toward data-intensive, adaptive, and resilience-oriented infrastructure analytics. This bibliometric review maps the research landscape at the intersection of these three domains. It quantifies publication trends, identifies thematic clusters, and interprets emerging fronts in smart-grid optimization research. A structured bibliometric protocol was applied to 28 peer-reviewed journal articles published between 2017 and 2025. Co-word analysis, citation analysis, thematic clustering, and citation burst interpretation were used to organize the evidence base. The analysis reveals rising publication activity after 2019 and three dominant thematic clusters. Digital twin research centers on simulation and cyber-physical representation, reinforcement learning emphasizes adaptive grid control, and cyber-resilience analytics focuses on vulnerability, disruption, and recovery. The field is moving toward integrated frameworks that combine digital twins and reinforcement learning for proactive, cyber-resilient grid optimization. These fronts suggest a growing need for real-time, explainable, and safety-aware analytics.

Real-time coordination of distributed energy resources is increasingly complex because distribution grids now operate with high variability, bidirectional power flows, and rapidly changing local constraints. Digital twins, graph neural networks, state estimation, and uncertainty-aware dispatch provide complementary capabilities for managing this complexity. This article proposes a layered digital-twin architecture that combines state estimation, graph neural network analytics, and uncertainty-aware dispatch for real-time DER coordination. The objective is to describe an architecture that is conceptual, modular, and suitable for active distribution network operation. The architecture is designed conceptually, with layers for physical grid mirroring, measurement fusion, topology-aware learning, and robust dispatch. A validation strategy is positioned as a future pathway rather than as an experimental contribution. The proposed architecture modularizes the DER coordination task into interoperable layers with distinct responsibilities and interfaces. Three tables are used to define the architecture layers, applicable GNN functions, and uncertainty-handling mechanisms. The architecture provides a blueprint for next-generation distribution management systems that require real-time observability, learning-based analytics, and robust decision support. It is intended to guide future implementation, simulation, and field validation.

Microgrids are increasingly relied upon to maintain supply during disruptions, but their resilience depends on timely islanding, accurate renewable forecasts, and efficient battery dispatch. Digital twins offer a platform to integrate these functions into a coordinated control environment. Existing microgrid control architectures often treat islanding, forecast uncertainty, and battery recovery as separate problems. An integrated conceptual framework that unifies them within a digital-twin environment is therefore needed. This article proposes a conceptual framework for digital-twin-assisted microgrid control that coordinates resilience-oriented islanding, renewable forecast error handling, and battery dispatch recovery. The framework is intended as a design-oriented contribution rather than an experimental validation. The framework is developed through conceptual synthesis of digital twin architectures, microgrid resilience theory, forecasting uncertainty modeling, and battery storage control. No experiments, simulations, field measurements, or empirical results are reported. The framework describes a multi-layered control architecture that continuously assesses resilience, triggers islanding under forecast uncertainty, and manages battery dispatch for post-islanding recovery. Four tables summarize digital twin functions, forecast error types, control architecture, and resilience indicators. The framework provides a design blueprint for resilient microgrid controllers that leverage digital twins. It can guide future simulation studies, hardware-in-the-loop testing, and field validations.

The emergence of 5G technology is revolutionizing the development of smart cities by providing unparalleled speed, ultra-low latency, and massive device connectivity. These advancements enable seamless integration of IoT devices, real-time monitoring systems, and innovative urban solutions. This paper investigates the role of 5G in addressing key urban challenges—such as network congestion, energy efficiency, and data security—while emphasizing its transformative potential for advancing smart city architectures. Particular attention is given to integrating 5G with real-time digital twins, which bridge the physical and digital worlds to optimize urban operations, improve resource allocation, and enhance public safety. Despite its promise, 5G deployment faces obstacles including high infrastructure costs, substantial energy consumption, and limited signal coverage. This study reviews current trends, existing implementations, and emerging issues surrounding 5G in smart cities, offering perspectives on its limitations and potential mitigation strategies. It underscores the importance of collaboration among stakeholders to fully unlock 5G’s capabilities and develop scalable, secure, and sustainable smart city solutions. 

Platforms based on digital twins are gaining widespread use in overseeing energy infrastructure, particularly within smart grid environments. Recent progress in artificial intelligence opens up new possibilities to build greater user confidence by strengthening predictive and prescriptive analysis capabilities and refining how users interact with these systems. This study introduces an innovative framework and architectural design for energy digital twins that combines data-driven techniques with knowledge-based elements. Machine learning-driven data integration and analysis are fused into a knowledge graph that captures asset condition information, forecasting results, and relevant domain expertise. This structure underpins a retrieval-augmented generation strategy, which powers an advanced conversational virtual assistant grounded in a large language model. The assistant delivers practical decision-making support for asset oversight and maintenance activities. Key parts of the suggested architecture have been aligned with readily available commercial tools to create a working prototype. This prototype was applied in a practical scenario involving a portion of the high-voltage energy network located in central Italy.

To effectively respond to ongoing global shifts—including globalization, climate change, and population expansion—contemporary urban areas need to integrate digital technologies into their governance and operations. This study explores a framework for urban digitalization, drawing inspiration from the digital twin initiative implemented in Rotterdam. It outlines data-handling approaches suitable for diverse data origins. The discussion also covers associated cybersecurity vulnerabilities and the possible exploitation of smart city infrastructure for military purposes. Finally, by examining publicly accessible datasets on air quality monitoring and electricity use in public lighting, the study compares the functional capabilities of Rotterdam’s digital twin with those of the digital platform developed for Brno. The analysis reveals that each municipality adopts a distinct digital transformation strategy, making a standardized, universal model impractical. Consequently, urban digitalization remains highly context-specific and individualized. At present, both platforms primarily enable the visualization of aggregated urban data. However, neither has yet incorporated sophisticated capabilities, such as artificial intelligence-supported decision-making or predictive modeling of urban phenomena. Nevertheless, they generate valuable datasets that hold significant potential for external stakeholders and third-party applications.