Kouhyar Sheida

The increasing shift towards decentralized energy systems have made microgrids (MGs) a key solution for improving energy reliability, resilience, and sustainability. However, managing the complexities of MG operations poses significant challenges. Traditional approaches often struggle to address these complexities, resulting in inefficiencies and compromised reliability. In response, this paper presents a digital twin (DT) framework designed for decision-making and real-time fault detection and diagnosis tested in a lab-scale DC microgrid. The DT model replicates the dynamics of the physical system (PS) under various fault conditions. An AI-driven Long Short-Term Memory (LSTM) model is integrated into the framework to improve health management. The performance of the system is assessed under both normal and emergency conditions using metrics such as Root Mean Square Error (RMSE) and confusion matrices. A comparative analysis between the DT and PS validates the DT’s ability to detect and diagnose faults accurately, demonstrating its potential to enhance microgrid operational resilience.

This research study presents a resilient control scheme for an islanded DC microgrid (DC MG) integrating solar photovoltaic (PV), battery storage (BESS), and piezoelectric (PE) energy harvesting modules. The microgrid (MG) case study represents an energy hub designed to provide electricity for lighting systems in transportation, roads, and other infrastructure. To enhance practicality, the PE is modeled using the real data captured from a traffic simulator. The proposed reinforcement learning (RL) method was tested against four severe and unexpected failure scenarios, including short circuit at the load side, sudden and severe change of load, open circuit, and converter failure. The performance of the controller was quantitatively compared with a conventional PI controller. The results show marginal improvement in one scenario and significant improvement in the other three, suggesting that the proposed scheme is a robust candidate for microgrids with high levels of uncertainty, such as those involving solar and PE harvesters.