AI-Driven Energy and Power Systems for Intelligent, Resilient and Sustainable Energy Futures

Deadline for manuscript submissions: 30 April 2027.

Guest Editor (1)

Elkhatib  Kamal
Dr. Elkhatib Kamal 
Laboratoire des sciences du numérique de Nantes (LS2N), École Centrale de Nantes, Nantes 44300, France
Interests: Artificial Intelligence for Energy Systems; Smart Grid Technologies; Power System Optimization and Control; Renewable Energy Integration; Energy Storage Systems; Machine Learning Applications in Power Systems; Energy Data Analytics; Distributed Energy Resource Management; Digital Twin for Energy Systems; Cyber-Physical Energy Infrastructure

Co-Guest Editors (5)

Abdelouhab  AITOUCHE
Prof. Dr. Abdelouhab AITOUCHE 
Automotive Engineering and Transportation Engineering, University of Sciences and Technology of Lille, Lille 59650, France
Interests: Diagnostic; FTC; automation; SoS; nonlinear control; FC; Renewable energy; IWT;
Mohan  Dolatabadi
Dr. Mohan Dolatabadi 
Optimization and Mathematical Modeling, Università degli Studi di Salerno, Fisciano SA 84084, Italy
Interests: Numerical Optimization
Reza  Ghorbani
Prof. Dr. Reza Ghorbani 
Renewable Energy Design Laboratory, Mechanical Engineering, University of Hawaii at Manoa, Hawaii 96822, USA
Interests: Renewable Energy; Dynamics; Control
Mohamed  Kouki
Dr. Mohamed Kouki 
Laboratoire Génie de Production-LGP, University of Toulouse, UTTOP, Tarbes 65000, France
Interests: Power Systems, Control, Modal Analysis, Energy Management, Microgrids
Ahmed  Ragab
Prof. Dr. Ahmed Ragab 
Lead AI Scientist, Natural Resources Canada – CanmetENERGY, Polytechnique Montréal, Montréal QC H3T 1J4, Canada
Interests: Artificial Intelligence & Machine Learning, Data & Decision Fusion, Causality Analysis, Supervisory
Website:

Special Issue Information

The global transition toward sustainable, resilient energy infrastructure is accelerating the transformation of conventional power systems into highly interconnected, data-rich, and adaptive smart energy ecosystems. In this context, recent advances in Artificial Intelligence (AI) and Machine Learning (ML) are reshaping the planning, operation, control, and optimization of modern energy and power systems. These technologies enable enhanced situational awareness, predictive analytics, autonomous decision-making, and intelligent coordination across generation, transmission, distribution, storage, and consumption layers.

AI-driven approaches are becoming essential for managing the increasing penetration of renewable energy sources, distributed energy resources, electric vehicles, and flexible demand-side participation within modern Smart grids. Furthermore, emerging paradigms such as digital twins, edge intelligence, energy big-data analytics, and cyber-physical system integration are unlocking new opportunities for real-time system optimization and resilience enhancement under uncertainty and complex operational constraints.

This Special Issue aims to provide a high-impact platform for researchers, engineers, and practitioners to present cutting-edge advances, innovative methodologies, and interdisciplinary applications of AI technologies in energy and power systems. Contributions addressing theoretical developments, practical implementations, system-level demonstrations, and policy-relevant insights are particularly encouraged. By bringing together diverse perspectives from academia and industry, the Special Issue will highlight transformative AI-enabled solutions supporting the evolution of intelligent, secure, efficient, and sustainable energy infrastructures worldwide.

Potential topics include, but are not limited to:
  • AI-based power system operation, control, and optimization
  • Machine learning for renewable energy forecasting (solar, wind, hybrid systems)
  • Deep learning techniques for load prediction and demand response
  • AI-enabled distributed energy resource management
  • Intelligent energy storage modeling, control, and scheduling
  • Digital twin technologies for smart energy systems
  • Edge computing and distributed intelligence for grid-edge applications
  • AI for resilience enhancement and fault detection in power networks
  • Cybersecurity frameworks using AI for critical energy infrastructure
  • Multi-energy system coordination using intelligent optimization methods
  • AI-driven electric vehicle charging infrastructure integration
  • Energy big-data analytics and predictive maintenance strategies
  • Explainable AI for power system decision support
  • Hybrid physics-informed and data-driven energy system modeling
  • AI applications in energy markets and flexibility services
  • Socio-economic and policy implications of AI adoption in energy systems

Published Papers (1 Papers)

Open Access

Article

30 July 2026

Normalized Source-Storage-Load Flexibility-Loss Indicators for Day-Ahead Scheduling of Renewable Virtual Power Plants

Renewable virtual power plants (VPPs) require day-ahead schedules that coordinate renewable generation, storage, gas turbines, and demand-side flexibility while retaining interpretable operating signals. This paper develops a normalized source-storage-load flexibility-loss-indicator (FLI) framework for renewable VPP scheduling. The indicators are engineering proxies for renewable-curtailment value loss, storage availability loss, and interruptible-load activation burden, rather than market-settled opportunity costs or a complete uncertainty-risk measure. The scalar objective combines net operating cost, operating coordination cost, and normalized FLI terms with reference scales fixed within each comparative setting. In a PJM-scaled day-ahead scheduling case, the baseline, scalarized multi-term reference, and proposed FLI schedules achieved net operating profits of 69,590.98 USD, 60,377.26 USD, and 66,856.81 USD. The proposed schedule reduced SOC boundary contacts from 9 to 3 and equivalent full cycles from 1.7253 to 1.6647, with a 3.93% profit concession relative to the baseline. Weight sensitivity, channel ablation, and eight representative operating and stress-test scenarios show state-dependent effects: the storage channel was active in seven scenarios, the interruptible-load channel under peak and flexibility-stressed conditions, and the renewable channel remained weak because curtailment was nearly zero. The framework provides a diagnostic coordination signal for renewable VPP operation.

Lai Jiang
Yufeng Guo*
Rui Bao
Qi Wang
Wei Xu
Yihang Ouyang
Yuxin Jiang
Lichaozheng Qin
Smart Energy Syst. Res.
2026,
2
(3), 10010; 
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