Human-in-the-Loop Safe Reinforcement Learning: Applications in Power and Energy Systems
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".
Deadline for manuscript submissions: closed (28 February 2025) | Viewed by 1040
Special Issue Editors
Interests: energy management systems; applications of machine learning in smart grids; operation and control of power and energy systems
Special Issues, Collections and Topics in MDPI journals
Interests: control of DC/AC microgrids; robust control of power converters; consensus control; virtual impedance shaping; power quality; non-linear control; electric vehicle control; hardware in loop control applications
Special Issue Information
Dear Colleagues,
It is our pleasure to invite submissions to the Special Issue on “Human-in-the-Loop Safe Reinforcement Learning: Applications in Power and Energy Systems”.
The integration of deep reinforcement learning (DRL) with human-in-the-loop systems represents a significant advancement in the optimization and management of power and energy systems. This synergy combines the adaptive learning capabilities of DRL with the safety assurances necessary for critical infrastructure environments, such as power and energy systems. Human-in-the-loop systems enrich this framework by incorporating human expertise and oversight, ensuring robust decision-making and operational resilience in dynamic energy landscapes.
This Special Issue aims to explore cutting-edge research and practical applications of human-in-the-loop safe RL in power and energy systems. We invite researchers from both academia and industry to submit original research articles, reviews, and case studies that advance our understanding of how human-in-the-loop safe RL can enhance the efficiency, reliability, and sustainability of power and energy systems. Our goal is to foster interdisciplinary dialogue and innovation at the intersection of AI, human factors, and energy infrastructure, paving the way for transformative advancements in the field.
Topics of interest for publication include, but are not limited to:
- Applications of artificial intelligence (AI) in the operation and control of power and energy systems;
- Building energy management systems;
- Charging stations with DRL;
- Decentralized and distributed operation and control of power and energy systems;
- Energy management systems;
- Integration of renewable energy sources and mobile loads;
- Integration of human expertise with AI in energy management;
- Operation and control of power and energy systems;
- Peer-to-peer energy trading in power systems;
- Novel safe RL algorithms and applications in power and energy systems;
- Multi-energy systems with combined cooling, heat, and power;
- Multiagent deep reinforcement learning applications.
Dr. Van-Hai Bui
Guest Editor
Dr. Shivam Chaturvedi
Dr. Srijita Das
Guest Editor Assistants
Manuscript Submission Information
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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. Algorithms is an international peer-reviewed open access monthly 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 1600 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
- artificial intelligence
- building energy management systems
- combined cooling, heat, and power systems
- deep reinforcement learning
- deep learning
- distributed energy resources
- distributed operation and control
- double auction
- game theory
- human in the loop machine learning
- energy management systems
- machine learning algorithms
- multi-agent reinforcement learning
- microgrids
- muti-energy system
- multi-agent system
- peer-to-peer communication optimization
- optimal energy trading
- safe reinforcement learning
- smart grid
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