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New Technologies in Wireless Power Management for Distribution Networks and...
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New Technologies in Wireless Power Management for Distribution Networks and Electric Vehicles

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 2255

Special Issue Editors

Dr. Kaiyang Liu

E-Mail Website
Guest Editor
Department of Computer Science, University of Victoria, Victoria, BC V8P 5C2, Canada
Interests: distributed networks; IoT
Dr. Heng Li

E-Mail Website
Guest Editor
School of Electronic Information, Central South University, Changsha 410087, China
Interests: battery management systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce a Special Issue dedicated to exploring the latest advancements and innovations in the field of "New Technologies in Wireless Power Management for Distribution Networks and Electric Vehicles". This Special Issue aims to showcase cutting-edge solutions and research breakthroughs in wireless power transfer (WPT) and energy management technologies for distribution networks and electric vehicles (EVs).

As the demand for renewable energy sources and electric power increases, the efficient and reliable management of distribution networks and electric vehicles becomes crucial. Wireless power management systems offer promising solutions for optimizing energy transfer, enhancing system flexibility, and improving overall network performance. This Special Issue seeks to bring together researchers, academicians, and industry professionals to present their original research, methodologies, and practical implementations in areas including (but not limited to):

Wireless power transfer technologies for distribution networks;
Wireless power management for renewable energy integration;
Wireless power transfer systems for electric vehicle charging;
Internet-of-batteries in distribution networks and electric vehicles;
IoT-driven battery management for electric vehicles;
IoT-driven energy management of distribution networks.

Dr. Kaiyang Liu
Dr. Heng Li
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. 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

  • wireless power transfer
  • network topology control
  • static and dynamic wireless charging
  • modeling, analysis, and design
  • energy efficiency

Published Papers (1 paper)

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Research

14 pages, 2109 KiB  
Article
ACC-RL: Adaptive Congestion Control Based on Reinforcement Learning in Power Distribution Networks with Data Centers
by Tairan Huang, Xiaojuan Lu, Dian Zhang, Haoran Cheng, Pingping Dong and Lianming Zhang
Energies 2023, 16(14), 5385; https://doi.org/10.3390/en16145385 - 14 Jul 2023
Cited by 4 | Viewed by 1441
Abstract
Modern data center power distribution networks place greater demands on the stability and reliability of power supply. Growing network computing demands and complex network environments can cause network congestion, which in turn leads to network traffic overload and power supply equipment overload. Therefore, [...] Read more.
Modern data center power distribution networks place greater demands on the stability and reliability of power supply. Growing network computing demands and complex network environments can cause network congestion, which in turn leads to network traffic overload and power supply equipment overload. Therefore, network congestion is one of the most important problems faced by data center power distribution networks. In this paper, we propose an approach called ACC-RL based on reinforcement learning (RL), which can effectively avoid network congestion and improve energy performance. ACC-RL models the congestion control task as a Partially Observable Markov Decision Process (POMDP). It is independent of the estimated value function and supports deterministic policies. It also sets the reward value function using real-time network information such as the transmission rate, RTT, and switch queue length, with the target transmission rate as the target equilibrium point. ACC-RL is highly general, can be trained on datasets running in different network environments, and generates a robust congestion control policy. The experimental results show that ACC-RL can solve the congestion problem without any predefined scenarios in different network environments. It can control the network traffic well, thus ensuring the stability and reliability of the power supply in the distribution network. We conduct network simulation experiments through NS-3. We set up different scenarios for experiments and data analysis in many-to-one, all-to-all, and long–short network environments. Compared with the popular rule-based congestion control algorithms such as TIMELY, DCQCN, and HPCC, ACC-RL shows different degrees of energy performance advantages in network metrics such as fairness, link utilization, and throughput. Full article
(This article belongs to the Special Issue New Technologies in Wireless Power Management for Distribution Networks and Electric Vehicles)
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