Intelligent Algorithms for High-Penetration New Energy

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 15 December 2024 | Viewed by 885

Special Issue Editors


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Guest Editor
School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 470074, China
Interests: automata theory; mathematical problems of artificial intelligence; complex networks; dynamical systems; fuzzy logic
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Engineering, RMIT University, Melbourne, VIC 3001, Australia
Interests: bilevel optimization; game theory; machine learning

Special Issue Information

Dear Colleagues, 

We are delighted to extend our invitation to you to submit your cutting-edge research in the field of intelligent algorithms for high-penetration new energy to our Special Issue entitled "Intelligent Algorithms for High-Penetration New Energy". 

With the increasing global focus on renewable energy sources, the development of intelligent algorithms holds significant promise for optimizing energy generation, storage, and utilization in high-penetration new energy systems. The advancements in this field have paved the way for the efficient integration of renewable energy into existing power infrastructures. 

This Special Issue aims to foster research activities in the realm of intelligent algorithms specifically tailored for high-penetration new energy systems. We encourage multidisciplinary contributions that showcase innovative algorithms and methodologies, addressing the distinctive characteristics and challenges associated with the integration of renewable energy sources into power networks.

Prof. Dr. Ming-Feng Ge
Dr. Chen Liu
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. 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

  • intelligent algorithms for high-penetration new energy
  • optimization techniques for renewable energy generation and storage
  • machine learning algorithms for renewable energy integration and control
  • data-driven forecasting models for renewable energy sources
  • intelligent energy management systems for high-penetration new energy
  • hybrid energy systems and their intelligent control
  • fault detection and self-healing algorithms for renewable energy systems
  • cybersecurity and privacy in high-penetration new energy systems

Published Papers (1 paper)

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Research

16 pages, 778 KiB  
Article
Distributed Control of Hydrogen-Based Microgrids for the Demand Side: A Multiagent Self-Triggered MPC-Based Strategy
by Tingzhe Pan, Jue Hou, Xin Jin, Zhenfan Yu, Wei Zhou and Zhijun Wang
Algorithms 2024, 17(6), 251; https://doi.org/10.3390/a17060251 - 7 Jun 2024
Viewed by 494
Abstract
With the global pursuit of renewable energy and carbon neutrality, hydrogen-based microgrids have also become an important area of research, as ensuring proper design and operation is essential to achieve optimal performance from hybrid systems. This paper proposes a distributed control strategy based [...] Read more.
With the global pursuit of renewable energy and carbon neutrality, hydrogen-based microgrids have also become an important area of research, as ensuring proper design and operation is essential to achieve optimal performance from hybrid systems. This paper proposes a distributed control strategy based on multiagent self-triggered model predictive control (ST-MPC), with the aim of achieving demand-side control of hydrogen-based microgrid systems. This architecture considers a hybrid energy storage system with renewable energy as the main power source, supplemented by fuel cells based on electrolytic hydrogen. The primary objective of this architecture is aiming at the supply and demand balance problem under the supply and demand relationship of microgrid, the service life of hydrogen-based microgrid energy storage equipment can be increased on the basis of realizing demand-side control of hydrogen energy microgrid system. To accomplish this, model predictive controllers are implemented within a self-triggered framework that dynamically adjusts the counting period. The simulation results demonstrate that the ST-MPC architecture significantly reduces the frequency of control action changes while maintaining an acceptable level of set-point tracking. These findings highlight the viability of the proposed solution for microgrids equipped with multiple types of electrochemical storage, which contributes to improved sustainability and efficiency in renewable-based microgrid systems. Full article
(This article belongs to the Special Issue Intelligent Algorithms for High-Penetration New Energy)
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