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Power generation, distribution, transmission, and consumption face ongoing challenges such as smart grid management, control, and operation, resulting from high energy demand, the diversity of energy sources, and environmental or regulatory issues. This paper provides a comprehensive overview of blockchain-based solutions for smart grid management, control, and operations. We systematically summarize existing work on the use and implementation of blockchain technology in various smart grid domains. The paper compares related reviews and highlights the challenges in the management, control, and operation for a blockchain-based smart grid as well as future research directions in the five categories: collaboration among stakeholders; data analysis and data management; control of grid imbalances; decentralization of grid management and operations; and security and privacy. All these aspects have not been covered in previous reviews.

INTRODUCTION: The increasing complexity of modern power grids, driven by the integration of distributed energy resources and dynamic operating conditions, presents significant challenges for stability assessment. Traditional stability analysis methods often fail to capture topological dependencies and nonlinear interactions among grid components, resulting in unreliable predictions. Furthermore, existing approaches such as static models and graph convolution networks lack effective node-level importance weighting, limiting their ability to distinguish between stable and unstable states.OBJECTIVES: This study aims to develop an advanced framework for power grid stability classification by integrating digital twin technology with Graph Attention Networks (GAT). The objective is to improve the modeling of inter-node relationships and enhance classification accuracy under complex grid conditions.METHODS: A digital twin-inspired graph model of the power grid is constructed, where nodes represent grid components and edges represent their interactions. A Graph Attention Network is employed to learn weighted inter-node dependencies using attention mechanisms, enabling effective differentiation between stable and unstable operating modes. The proposed framework is evaluated in an offline, simulation-based environment using the Smart Grid Stability dataset.RESULTS: Experimental results demonstrate the effectiveness of the proposed approach, achieving an accuracy of 0.9640, precision of 0.9411, recall of 0.9607, F1-score of 0.9508, and ROC-AUC of 0.9958. Comparative analysis indicates that the proposed model outperforms conventional methods, including Artificial Neural Networks (ANN), Deep Neural Networks (DNN), and Random Forest, in overall classification performance.CONCLUSION: The proposed digital twin-inspired GAT framework provides accurate and reliable offline stability classification, significantly improving upon existing methods. However, challenges related to scalability for larger grid systems and real-time cyber–physical synchronization remain, highlighting important directions for future research.

The rapid global shift toward renewable energy necessitates innovative solutions to address the intermittency and variability of solar and wind power. This study presents a comprehensive review and framework for deploying Integrated Energy Storage Systems (IESSs) to enhance grid efficiency and stability. By leveraging a Multi-Criteria Decision Analysis (MCDA) framework, this study synthesizes techno-economic optimization, lifecycle emissions, and policy frameworks to evaluate storage technologies such as lithium-ion batteries, pumped hydro storage, and vanadium flow batteries. The framework prioritizes hybrid storage systems (e.g., battery–supercapacitor configurations), demonstrating 15% higher grid stability in high-renewable penetration scenarios, and validates findings through global case studies, including the Hornsdale Power Reserve (90–95% round-trip efficiency) and Kauai Island Utility Cooperative (15,000+ cycles for flow batteries). Regionally tailored strategies, such as Kenya’s fast-track licensing and Germany’s H2Global auctions, reduce deployment timelines by 30–40%, while equity-focused policies like India’s SAUBHAGYA scheme cut energy poverty by 25%. This study emphasizes circular economy principles, advocating for mandates like the EU’s 70% lithium recovery target to reduce raw material costs by 40%. Despite reliance on static cost projections and evolving regulatory landscapes, the MCDA framework’s dynamic adaptation mechanisms, including sensitivity analysis for carbon taxes (USD 100/ton CO2-eq boosts hydrogen viability by 25%), ensure scalability across diverse grids. This work bridges critical gaps in renewable energy integration, offering actionable insights for policymakers and grid operators to achieve resilient, low-carbon energy systems.

The intermittent and stochastic nature of Renewable Energy Sources (RESs) necessitates accurate power production prediction for effective scheduling and grid management. This paper presents a comprehensive review conducted with reference to a pioneering, comprehensive, and data-driven framework proposed for solar Photovoltaic (PV) power generation prediction. The systematic and integrating framework comprises three main phases carried out by seven main comprehensive modules for addressing numerous practical difficulties of the prediction task: phase I handles the aspects related to data acquisition (module 1) and manipulation (module 2) in preparation for the development of the prediction scheme; phase II tackles the aspects associated with the development of the prediction model (module 3) and the assessment of its accuracy (module 4), including the quantification of the uncertainty (module 5); and phase III evolves towards enhancing the prediction accuracy by incorporating aspects of context change detection (module 6) and incremental learning when new data become available (module 7). This framework adeptly addresses all facets of solar PV power production prediction, bridging existing gaps and offering a comprehensive solution to inherent challenges. By seamlessly integrating these elements, our approach stands as a robust and versatile tool for enhancing the precision of solar PV power prediction in real-world applications.

The micro grid idea provides for the lack of several reversing switches to unitary AC-DC grid that enables connection and charges (loads) to the electrical systems with changeable regenerative AC and CC sources. Safe operation and gadget safety involve digital integration with utilities/grid through power converters. Enhanced client reliability, decreased input losses, local voltages are supported, and waste heat efficiency increased, voltage drop or interruptible supply of electricity can be customized to satisfy their unique customer demands. Work at present Analyses the performance in grid tie mode of hybrid AC/DC systems. Here are PV systems, PV systems, For the construction of microgrids wind turbine generators and batteries are employed. Convert procedures for the correct coordination of AC sub-grids to DC subs-grids have also been established for converters. MATLAB/SIMULINK environment results are generated.

Low-carbon energy transitions taking place worldwide are primarily driven by the integration of renewable energy sources such as wind and solar power. These variable renewable energy (VRE) sources require energy storage options to match energy demand reliably at different time scales. This article suggests using a gravitational-based energy storage method by making use of decommissioned underground mines as storage reservoirs, using a vertical shaft and electric motor/generators for lifting and dumping large volumes of sand. The proposed technology, called Underground Gravity Energy Storage (UGES), can discharge electricity by lowering large volumes of sand into an underground mine through the mine shaft. When there is excess electrical energy in the grid, UGES can store electricity by elevating sand from the mine and depositing it in upper storage sites on top of the mine. Unlike battery energy storage, the energy storage medium of UGES is sand, which means the self-discharge rate of the system is zero, enabling ultra-long energy storage times. Furthermore, the use of sand as storage media alleviates any risk for contaminating underground water resources as opposed to an underground pumped hydro storage alternative. UGES offers weekly to pluriannual energy storage cycles with energy storage investment costs of about 1 to 10 USD/kWh. The technology is estimated to have a global energy storage potential of 7 to 70 TWh and can support sustainable development, mainly by providing seasonal energy storage services.

This abstract describes the smart grid management system is an emerging technology that utilizes machine learning algorithms for efficient energy distribution. The paper presents an overview of the architecture, benefits, and challenges of smart grid management systems. The paper also discusses various machine learning algorithms used in smart grid management systems such as neural networks, decision trees, and Support Vector Machines (SVM). The advantages of using machine learning algorithms in smart grid management systems include increased energy efficiency, reduced energy wastage, improved reliability, and reduced costs. The challenges in implementing machine learning algorithms in smart grid management systems include data security, privacy, and scalability. The paper concludes by discussing future research directions in smart grid management systems based on machine learning algorithms.

In response to the growing need for enhanced energy management in smart grids in sustainable smart cities, this study addresses the critical need for grid stability and efficient integration of renewable energy sources, utilizing advanced technologies like 6G IoT, AI, and blockchain. By deploying a suite of machine learning models like decision trees, XGBoost, support vector machines, and optimally tuned artificial neural networks, grid load fluctuations are predicted, especially during peak demand periods, to prevent overloads and ensure consistent power delivery. Additionally, long short-term memory recurrent neural networks analyze weather data to forecast solar energy production accurately, enabling better energy consumption planning. For microgrid management within individual buildings or clusters, deep Q reinforcement learning dynamically manages and optimizes photovoltaic energy usage, enhancing overall efficiency. The integration of a sophisticated visualization dashboard provides real-time updates and facilitates strategic planning by making complex data accessible. Lastly, the use of blockchain technology in verifying energy consumption readings and transactions promotes transparency and trust, which is crucial for the broader adoption of renewable resources. The combined approach not only stabilizes grid operations but also fosters the reliability and sustainability of energy systems, supporting a more robust adoption of renewable energies.

This paper reviews alarm processing methods in electrical power systems, focusing on evolving strategies beyond traditional fault analysis to accommodate modern grid complexities. Historically, alarm processing has predominantly aimed at fault analysis, increasingly merging with technological advances in communication and computing. However, it still needs to fully meet the challenges posed by the dynamic characteristics of modern power systems. This review points out certain inadequacies in current practices, notably their limited adaptation to new grid conditions. The authors propose a novel generation of alarm processing methodologies designed for future grids, emphasizing managing rare events and enhancing operator decision-making through advanced anomaly detection and explainable artificial intelligence. This synthesis presents a prospective direction for future research and applications in alarm processing, advocating for methodologies better suited to supporting system operators amidst technological advancements.

The emergence of electric vehicles (EVs) as key elements in the decarbonization of transportation demands a new class of intelligent infrastructure capable of optimizing charging behavior while maintaining power system stability. This paper proposes a novel Scalable Cloud-Based Continuous Monitoring Platform (SC-CMP) designed to support real-time optimization of microgrid operations, particularly in EV-dense and renewable-integrated environments. By fusing cloud computing, machine learning (ML), and artificial intelligence (AI) with Internet of Things (IoT) data acquisition, SC-CMP enables continuous monitoring, predictive scheduling, and adaptive energy management across distributed power networks. Unlike conventional systems, SC-CMP supports both centralized and decentralized microgrid architectures, providing scalable support for dynamic load balancing, V2G coordination, and resilient energy dispatch. Simulation and validation are performed using a real-world dataset of 3395 EV charging sessions across 105 stations, demonstrating SC-CMP’s superiority over existing AI/ML baselines. Quantitatively, the platform achieves 97.34% predictive accuracy, 96.81% grid stability improvement, 94.5% resource allocation efficiency, 93% scalability, and 95.2% data privacy assurance. These outcomes position SC-CMP as a comprehensive, adaptive, and cost-effective solution for microgrid-oriented EV integration, offering substantial advances in resilient power distribution, renewable energy utilization, and sustainable electric mobility. The platform serves as a foundation for next-generation microgrid control systems that demand real-time intelligence, scalability, and reliability across evolving smart grid landscapes.