Towards Autonomous Energy Systems: Digital Twins, Machine Learning, and Resilience

Autonomy in energy systems demands intelligent, adaptive and resilient infrastructures. Digital twins and machine learning are emerging technologies that revolutionize the way energy systems are modelled, monitored and optimized in real time. Predictive maintenance, fault detection and dynamic control are crucial factors in maintaining the reliability and operational efficiency of the system and these tools help in achieving it.

With growing challenges like integration of renewables, climate change and cyber threats, resilience becomes important. This special edition is a sincere effort to accumulate cutting edge research to bridge the gap between data driven, intelligent and resilient energy systems, with a vision of developing fully autonomous and sustainable energy ecosystems.

Goal:

The primary goal of this special edition is to explore and showcase innovative advancements in autonomous, intelligent and resilient modern day energy systems. With the global energy landscape increasing with respect to integrated renewable sources, decentralized energy generation and complex grid dynamics, there is an impending need to develop technologies that are smarter and better adaptive to energy infrastructures. This edition highlights the role of digital twin in developing real time, physics based and data driven models of energy systems and establishments to enable simulation, monitoring and optimization. Artificial intelligence and machine learning techniques are employed largely for demand forecasting, control strategies, fault diagnostics, and decision making for dynamic energy systems and environments. The goal of resilience is to anticipate, absorb, adapt and recover from disruptions like climate changes, cyber attacks and operational uncertainties. This special issue majorly aims to develop scalable, intelligent and robust energy solutions for autonomous and sustainable energy ecosystem through interdisciplinary and collaborative work of industry, academy and policy professionals.

Scope and Information for Authors:

This special issue invites original research articles, reviews and case studies on advancements in the field of autonomous energy systems through digital twin technology, machine learning and resilient strategies. Topics of interest includes modeling of smart grid, renewable energy systems, integrated systems, fault detection, predictive maintenance, control systems, decentralized control, physical systems, cyber-physical systems, resilience assessment techniques, resilience assessment framework. Contributions comprising of interdisciplinary approaches, real world applications and policy frameworks are highly encouraged. Authors from academia, research institutions and industries are welcome to submit manuscripts presenting novel techniques, validated simulations, experimental results and insightful theoretical analysis.

This Research Topic focuses on how the integration of Digital Twins, Machine Learning, and system resilience can drive the transition to sustainable energy systems. We welcome manuscripts that enhance real-time decision-making, optimize resource use, or improve system reliability in support of decentralized, low-carbon energy infrastructures. Submissions must clearly demonstrate relevance to sustainable energy goals. Algorithm-focused studies without application to energy systems will not be considered.