Low-Carbon Energy Systems Under Uncertainty: Forecasting, Optimization, Control, and Flexibility

The field of low-carbon energy systems is rapidly evolving as societies strive to decarbonize their infrastructures through widespread integration of renewables, electrification of transport, and the digital transformation of energy networks. These advancements introduce substantial uncertainties, stemming from the variability of renewable energy outputs, dynamic demand patterns, and increasingly complex market and system configurations. While traditional deterministic approaches have, until recently, provided the foundation for operations and planning, they are increasingly insufficient for the challenges posed by modern, highly variable environments. Recent studies demonstrate that combining probabilistic forecasting, flexible resource management, and advanced optimization methodologies can significantly enhance system reliability, efficiency, and resilience. Yet, a persistent gap remains: how can these methodological advancements be effectively translated into validated operational improvements for real-world, next-generation energy systems?This Research Topic aims to catalyze research and practical innovation in forecasting, optimization, and real-time control strategies that explicitly address uncertainty in low-carbon energy operations. The goal is to develop robust and practically deployable approaches—incorporating probabilistic and multi-horizon forecasting, uncertainty quantification, risk-aware optimization, and safety-aware or constrained control—to tackle the complexities that arise from the integration of renewables, distributed energy resources, storage, electric vehicles, and cross-sector coupling. The Research Topic particularly encourages contributions that bridge methodological development and practical application, demonstrating the tangible operational benefits of uncertainty-aware forecasting and optimization. Authors are invited to address core questions such as: How does forecasting uncertainty propagate into downstream operational decisions? What new strategies enable flexible and resilient energy management in the face of these uncertainties? And how can digitalization and hybrid modeling approaches support trustworthy decision-making across diverse energy infrastructures?The Research Topic is dedicated to advancing applied and interdisciplinary work on operational management under uncertainty, with a strong focus on validated, real-world relevance. Submissions are expected to demonstrate operational impact through validation using real datasets, realistic test cases, or field-relevant benchmarks, rather than focusing solely on algorithmic novelty. The scope is designed to include, but is not limited to, the following themes:- Probabilistic and multi-horizon forecasting for load, renewable generation, prices, and outages- Flexibility management, including energy storage, demand response, electric vehicles, and aggregation strategies- Uncertainty-aware optimization and control, such as dispatch, scheduling, model predictive control, and safe reinforcement learning- Integrated multi-energy systems and sector coupling, spanning power, heat, gas, and hydrogen- Resilience and risk-aware operation, including reliability assessment and strategies for extreme events- Digital twins and hybrid cyber-physical modeling to support operational decision-making- Asset and reliability analytics for maintenance and operational support- Trustworthy deployment, including interpretability, privacy, cybersecurity, and trust in operational tools Topic editors: Nikolay Hinov, Dardan Klimenta, Jozef Ritonja Source: https://www.frontiersin.org/research-topics/79387/low-carbon-energy-systems-under-uncertainty-forecasting-optimization-control-and-flexibility