Abstract: Modern electric power systems increasingly rely on Supervisory Control and Data Acquisition (SCADA) infrastructures to ensure reliable generation, transmission, and distribution of energy across geographically distributed assets. However, conventional SCADA systems remain largely reactive, offering limited predictive capability, fragmented cybersecurity monitoring, and delayed fault localization. These limitations expose critical grid infrastructure to increased downtime, cascading failures, and cyber-physical threats. This paper presents an AI-driven SCADA Grid Intelligence platform that integrates real-time telemetry ingestion, machine learning–based fault prediction, cybersecurity health monitoring, and decision-support analytics within a unified architecture. The proposed system leverages multi-modal SCADA data streams, ensemble learning models, and automated risk assessment to predict electrical faults, detect anomalous cyber-communication behavior, and optimize maintenance actions before failures occur. A fully functional prototype has been implemented and evaluated across multiple simulated and semi-realistic grid environments, including wind, solar, and hybrid energy sites. Experimental results demonstrate significant improvements in detection accuracy (up to 89% F1-score), early warning lead time (average 35 minutes), and measurable reductions in downtime and operational cost. The results validate the feasibility of AI-augmented SCADA intelligence as a scalable solution for enhancing grid resilience, cybersecurity compliance, and operational efficiency in modern power systems.

Abstract: A smart grid is an advanced energy distribution system that utilizes digital communication, analytics, and automation to enhance the efficiency and sustainability of the electricity grid. Its implementation is vital for improving energy efficiency, reducing carbon emissions, and managing increasing power demands. In Oman, over 90% of power generation relies on gas turbines and fossil fuels. The rapid development in the region has put pressure on the current grid, making the adoption of smart grid technology essential for sustainable progress. In this research, we conducted a qualitative and structural study using Delphi and interpretive structural modeling (ISM) methods instead of relying on statistical analysis. Our objective was to identify the barriers to implementing smart grids. We started with a literature review to pinpoint these obstacles. Based on our analysis, we provide recommendations to address the identified challenges. Finally, we analyzed the results from the Delphi rounds using ISM, a well-established approach for determining the relationships between specific elements that define a problem or issue.

Abstract: The Smart Grid Market is experiencing rapid growth, driven by the increasing demand for energy efficiency, sustainable power solutions, and resilient electricity networks. Advanced technologies such as smart meters, IoT-enabled devices, and automation systems are transforming traditional power grids into intelligent, responsive infrastructures. Key market drivers include rising investments in renewable energy integration, smart city initiatives, and digital grid modernization across residential, commercial, and industrial sectors. While challenges such as cybersecurity concerns and high deployment costs persist, the market is poised for substantial expansion, with North America leading in adoption and Asia-Pacific emerging as the fastest-growing region. Industry players are focusing on innovation, strategic partnerships, and the integration of distributed energy resources to enhance operational efficiency and reliability. Overall, smart grids are set to play a crucial role in shaping the future of sustainable, efficient, and resilient energy systems worldwide. Access Full Report- https://www.nextmsc.com/report/smart-grid-market

Abstract: Under China’s “dual-carbon” strategic goals and the advancement of smart city development, the rapid adoption of electric vehicles (EVs) has deepened the spatiotemporal coupling between transportation networks and distribution grids, posing new challenges for integrated energy systems. To address this, we propose a collaborative optimization framework for power–transportation coupled networks that integrates multi-modal data with physical priors. The framework constructs a joint feature space from traffic flow, pedestrian density, charging behavior, and grid operating states, and employs hypergraph modeling—guided by power flow balance and traffic flow conservation principles—to capture high-order cross-domain coupling. For prediction, spatiotemporal graph convolution combined with physics-informed attention significantly improves the accuracy of EV charging load forecasting. For optimization, a hierarchical multi-agent strategy integrating federated learning and the Alternating Direction Method of Multipliers (ADMM) enables privacy-preserving, distributed charging load scheduling. Case studies conducted on a 69-node distribution network using real traffic and charging data demonstrate that the proposed method reduces the grid’s peak–valley difference by 20.16%, reduces system operating costs by approximately 25%, and outperforms mainstream baseline models in prediction accuracy, algorithm convergence speed, and long-term operational stability. This work provides a practical and scalable technical pathway for the deep integration of energy and transportation systems in future smart cities.

Abstract: This study analyzes controllability and observability in cyber-physical smart grid systems under Denial-of-Service (DoS) attacks. A practical real-time system based on Supervisory Control and Data Acquisition (SCADA) technology was constructed to represent a three-bus smart grid. The Telnet protocol has been used to analyze the impact of the attacks on controllability and observability. Real-time cybersecurity strategies were applied to protect system components and restore their performance during the attack. The system was tested under three types of cyberattacks: a Telnet attack on port 23, a Flood attack over Modbus TCP, and an ICMP attack using hping3. The system was evaluated using controllability and observability matrices, and the theories were validated using practical simulations. The experiments were conducted on three different electrical networks: a three-bus network, a nine-bus network, and a fourteen-bus network to measure the system’s responsiveness. The results showed that cyberattacks significantly affected system stability; however, the implementation of cybersecurity protection mechanisms enabled the system to maintain controllability and observability during the attacks. This study proposes a framework for improving smart grid response to cyberattacks while maintaining controllability and observability. A realistic SCADA environment supported by packet analysis tools such as Wireshark was used to monitor system behavior during attacks. The results provide a new contribution to the research on protecting cyber-physical smart grid systems under practical attack conditions. Experimental and simulation results demonstrated that the proposed defensive control mechanisms effectively reduce rotational speed disturbances caused by cyberattacks on actuators by more than 80%. The system also proved capable of maintaining controllability and observability through full-rank matrices, even under coordinated attacks. Furthermore, real-time SCADA system experiments showed that the average system response time did not exceed 1.8 seconds, confirming the efficiency of the proposed framework in maintaining system stability under attack.

Abstract: The increasing deployment of smart meters across residential zones has generated an abundance of granular energy consumption data. However, accurately forecasting such data remains challenging due to its inherent heterogeneity, context-dependence, and nonlinearity. Traditional machine learning models often fall short in capturing the underlying causal dynamics and fuzzy uncertainties present in household-level energy use. To overcome this, we present a hybrid forecasting framework, which combines neural operator architecture, fuzzy logic-based interpretability, and causal discovery. This helps the framework learn structured representations from high-dimensional, multi-contextual data. We tested our framework on a real-world dataset of 5567 households’ smart meters forecasting data, in the city of London. Our model performs better than other traditional regressors with a test R2 score of 0.8788, which is about 30 % better than other baseline techniques evaluated. The model shows how our design helps people understand the results better. It identifies important factors that affect energy use, such as humidity, maximum temperature, and household energy. This work makes energy demand forecasts more accurate and easier to explain. It also provides a foundation for using similar models in areas like smart grids, sustainable cities, and renewable energy systems, where cause and uncertainty exist together.