Navigating Energy Trends: Exploring Control and Operation in Autonomous Energy Systems
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "I: Energy Fundamentals and Conversion".
Deadline for manuscript submissions: 5 May 2025 | Viewed by 192
Special Issue Editor
Interests: intelligent vehicular technologies; hybrid controls; customized design and manufacture; mechatronic system optimization; green-energy exploration
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue aims to delve into the dynamic realm of autonomous energy systems and investigate the multifaceted aspects of their control and operation. As the global energy landscape undergoes a transformative shift towards sustainability and autonomy, this scope focuses on the intersection of advanced technologies, artificial intelligence, and energy infrastructure.
Our scope encompasses research that addresses the intricate challenges and opportunities associated with the control and operation of autonomous energy systems. This includes, but is not limited to, the following:
Intelligent Control Algorithms: analyzing cutting-edge algorithms that empower autonomous systems to make real-time decisions, optimize energy usage, and adapt to changing conditions.
Integration of renewable sources: exploring strategies for seamlessly integrating renewable energy sources into autonomous systems, ensuring reliability, efficiency, and resilience.
Cybersecurity in energy networks: investigating robust cybersecurity measures to safeguard autonomous energy systems from potential threats and vulnerabilities.
Human–machine interaction: examining the role of human–machine collaboration in the effective control and operation of autonomous energy systems, considering user interfaces, decision support systems, and ethical implications.
Case studies and field applications: presenting real-world applications, success stories, and challenges faced in implementing autonomous energy systems across diverse environments.
This Special Issue aspires to provide a comprehensive platform for researchers, engineers, and policymakers that fosters a deeper understanding of the intricate dynamics surrounding the control and operation of autonomous energy systems in the pursuit of a sustainable future.
Prof. Dr. Chung-Neng Huang
Guest Editor
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. Energies 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 2600 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
- autonomous/optimal/metaheuristic controls
- zero-emission technologies and renewable/green energy
- strategies/policies/methodologies in cybersecurity
- human–machine collaboration design
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.
Further information on MDPI's Special Issue polices can be found here.
Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Optimizing Autonomous Multi-UAV Path Planning for Inspection Missions: A Comparative Study of Genetic and Stochastic Hill Climbing Algorithms
Authors: Faten Aljalaud; Yousef Alohali
Affiliation: Computer Science Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11564, Saudi Arabia
Abstract: Effective path planning is essential for multi-UAV inspection missions. This paper presents the first application of the Genetic Algorithm (GA) and Hill Climbing (HC) to multi-UAV inspection of indoor pipelines. To address the unique challenges of this problem, we develop and implement problem-specific repair heuristics to ensure the feasibility of solutions generated by GA and HC, preventing invalid path assignments. Given the critical importance of energy consumption in UAV applications due to limited battery capacity, we evaluate performance based on energy consumption, total travel distance, and maximum tour length. Our experiment used the Cra-zyswarm simulation platform to demonstrate that GA reduces total travel distance by 22% and energy consumption by 23% compared to HC, which often suffers from local optima. GA consist-ently outperforms HC, the Greedy Algorithm, and the Random Strategy, achieving at least a 13% improvement in travel distance, energy consumption, and maximum tour length across various maps and UAV configurations while ensuring balanced workloads among UAVs. These results underscore the suitability of GA for complex path planning tasks in multi-UAV inspection mis-sions, mainly where energy efficiency and workload distribution are critical.
