The islanded microgrids increasingly depend on renewable energy sources for power generation and introduce significant frequency control challenges due to the renewable sources’ intermittent nature and low system inertia. Traditional energy storage systems, though employed and effective for frequency stabilization, are often limited by high costs and power density requirements. Accounting issues of microgrid frequency performance under stochastic renewable energy source integration and limitations of conventional energy storage systems, this paper proposes a virtual inertia control strategy that leverages electric vehicle battery storage, supported by a cascade proportional integral derivative-reinforcement learning-based auxiliary controller, to enhance frequency regulation of the load frequency control system in an islanded microgrid. The proposed cascade proportional integral derivative-reinforcement learning-based virtual inertia controller is implemented and tested in a MATLAB/Simulink environment and evaluated under diverse operating conditions involving dynamic load and renewable energy source disturbances, while comparing with other control strategies. The comparative results demonstrate that the proposed virtual inertia control strategy outperforms other compared controllers in terms of frequency stability and overall dynamic response.
Athira Mohan, Amith Khandakar, S. M. Muyeen· Journal of Energy Storage· 0 citations
Connected Vehicle (CV) networks have emerged as key enablers of next-generation intelligent transportation systems, improving road safety and operational efficiency through real-time data exchange. However, large-scale adoption remains limited due to two fundamental challenges: 1) an unclear stakeholder value proposition and 2) persistent privacy concerns arising from continuous data collection. In parallel, conventional road condition monitoring methods are slow, expensive, and unable to provide continuous large-scale coverage. This work proposes a scalable and privacy-preserving framework that leverages smartphone-based sensing as a practical surrogate for connected vehicle data. Unlike infrastructure-dependent or single-modality approaches, the proposed method integrates widely available mobile devices with multimodal data sources to enable cost-effective, deployable large-scale road monitoring. This improves immediate real-world applicability while strengthening the value proposition needed for broader CV adoption. The research involves four main aspects. First, it has produced large-scale multimodal datasets combining smartphone inertial sensing, GPS, vision-based inputs, geographic information, and environmental conditions to capture diverse real-world driving scenarios. Second, it has used robust machine learning models to fuse heterogeneous data for accurate road anomaly detection under noisy and variable conditions. Third, it has proposed a layered privacy-preserving framework combining federated learning, contextual k-anonymity, and differential privacy to address non-IID vehicular data while ensuring strong privacy guarantees. Privacy implications are further analyzed using the IEEE Digital Privacy Model. Finally, the framework is validated through a cloud-based monitoring system and a sensing module designed to handle smartphone orientation variability in real-world deployments. In addition, the study identifies a critical gap in standardized metrics for evaluating privacy-preserving methods and proposes the need for a unified privacy score to enable systematic comparison. This research contributes a unified framework that combines scalable sensing, multimodal intelligence, and layered privacy mechanisms to balance detection performance, privacy preservation, and deployment feasibility. Overall, the proposed system establishes a scalable, privacy-aware, and deployable approach for road condition monitoring that supports intelligent transportation systems while addressing key barriers to connected vehicle adoption.
Amith Khandakar· cIRcle (University of Britis...· 0 citations