Optimizing Smart Microgrid Resiliency Through Advanced Digital Twin Modeling

In recent years, grid modernization has become crucial for many utilities due to the need for upgrading outdated infrastructure, assimilating renewable energy sources, catering to increasing electricity demand, improving resilience against climate-change-related disasters/events and cyber threats, enhancing energy efficiency, and facilitating new technology integration. It could underpin the transition to a low-carbon economy by mitigating greenhouse gas emissions. Among potential grid modernization solutions, Smart Energy Hubs play a substantial role for utilities to efficiently manage diverse energy resources, including renewables, energy storage, and demand response technologies, optimizing energy flows and bolstering grid reliability and resiliency.
Smart microgrids integral part of Smart Energy Hubs are pivotal for utilities and industries evolving towards Industry 4.0 and 5.0 paradigms where advanced solutions such as Artificial Intelligence (AI), merge with customer-centric approaches. These smart microgrids, not only centralize various energy sources, storage, and distribution networks, but also endorse adaptable, sustainable, resilient, and human-centric service models. Nonetheless, it is essential to take into account smart microgrids’ techno-economic challenges such as resilience enhancement, real-time condition monitoring, O&M, installation, and planning cost minimizations, cybersecurity, and expertise training through a novel effective solution.
Digital Twins (DTs) have recently emerged as one of the promising solutions to address the above-mentioned challenges when a smart microgrid needs to be designed, developed, operated, and well-maintained. These platforms enable proactive scenario testing, offer detailed system insights, and support emergency decision-making. As such, effective digital twinning processes for smart microgrids could enhance their resilience, streamline installations, curtail O&M costs, simplify complexities, and facilitate workforce training.
Hence, a proper Digital Twin engine needs to be designed and developed to accurately mirror physical infrastructures within the microgrid cogeneration assets, and support dynamic and real-time operating scenario testing that is crucial for emergency response. The design and implementation of such a Digital Twin Engine for smart microgrids necessitate thoughtful decisions on modeling approaches, simulation tool selection, real-time data migration, replication, and mitigation that could negatively affect the system’s performance in supporting the real system.