AI-Driven Low-Carbon Sustainable Energy Systems: System Design, Computational Strategies, and Emerging Innovations
A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".
Deadline for manuscript submissions: 12 March 2026 | Viewed by 525
Special Issue Editors
Interests: distributed control; energy internet; energy management system; machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: integration of artificial intelligence and smart grid technologies; power system source-load forecasting
Interests: AI-driven optimization under uncertainty; green electricity-hydrogen-chemical systems engineering
Special Issue Information
Dear Colleagues,
The dual imperatives of climate urgency and the energy transition demand transformative approaches to decarbonize global energy systems, and the integration of AI-driven methodologies with multi-energy networks has emerged as a pivotal pathway toward achieving carbon neutrality while maintaining energy resilience. This Special Issue focuses on paradigm-shifting innovations spanning intelligent system design, advanced computational frameworks, and cross-domain technological synergies that redefine sustainable energy ecosystems.
From a systems engineering perspective, AI-driven digital twins enable the dynamic co-optimization of interconnected energy vectors to increase renewable energy penetration through predictive grid adaptation and multi-timescale flexibility management. From a device perspective, machine learning-enhanced power electronics and intelligent energy storage systems fundamentally change the operational boundaries of distributed energy resources, enabling unprecedented efficiencies in resource-constrained environments. From a cyberspace perspective, IoT architectures and edge computing facilitate the convergence of the cyber and the physical, creating self-organizing energy networks with distributed autonomy.
While the existing literature has established foundational models for renewable integration, critical gaps persist in addressing the complexity of AI-embedded energy systems. This Special Issue aims to bridge these gaps by curating cutting-edge research that advances both theoretical frameworks and practical implementations. We seek original papers with novel contributions and impacts on computational efficiency and model accuracy, system-wide decarbonization and localized energy justice, and technological breakthroughs and regulatory adaptation.
We invite original research and comprehensive reviews exploring, but not limited to:
- AI-based charging demand forecasting and dispatch optimization for electric vehicles;
- Research on intelligent decision-making mechanisms for multi-energy collaborative optimization in integrated energy systems;
- Applications of reinforcement learning in the coordinated control of distributed energy resources;
- AI-enhanced stability analysis for modern power systems;
- The exploration of AI-driven intelligent decision-making for dynamic pricing and demand response in electricity markets;
- Multi-modal digital twin modeling for integrated energy systems;
- Applications of deep learning in energy systems;
- The AI-driven design of energy market mechanisms;
- AI-based model construction and the co-scheduling of virtual power plants.
Dr. Ning Zhang
Prof. Dr. Yan Juan
Dr. Chao Ning
Dr. Yumeng Song
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- AI-driven optimization
- reinforcement learning
- integrated energy systems
- deep learning
- digital twin modeling
- virtual power plants
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