Digital Modeling, Operation and Control of Sustainable Energy Systems

The global transition towards sustainable energy systems necessitates the seamless integration of variable renewable sources like solar and wind. However, the intermittent nature of renewables and the complex dynamics of power electronics pose significant challenges to system stability and reliable operation. Artificial intelligence, with its powerful capabilities in prediction, optimization, and adaptive control, has emerged as a pivotal enabler for managing the inherent complexity and uncertainty of modern hybrid renewable energy systems (HRES). This Reprint presents a collection of high-quality research addressing the design, operation, and optimization of AI-based energy management systems (EMS) for HRES. The contributions explore cutting-edge applications of machine learning, deep learning, and advanced control algorithms to enhance the efficiency, reliability, and economic viability of integrated energy systems. Topics covered include predictive maintenance for system components, intelligent forecasting of renewable generation and load demand, multi-objective optimization for real-time dispatch and storage control, and the development of digital twin frameworks for system simulation and resilience analysis. By presenting novel methodologies, empirical case studies, and quantitative findings, this volume provides significant new insights and practical tools. It demonstrates how AI-driven EMS can unlock greater energy autonomy, facilitate higher penetrations of renewables, and contribute decisively to the development of smarter, more resilient, and sustainable power grids.