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Net-Zero Energy Industry: Renewable Energies, Microgrids, Hydrogen, Electrification of Transportation and Heating

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A: Sustainable Energy".

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 779

Special Issue Editors

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Guest Editor
Centre for New Energy Transition Research, Federation University Australia, Ballarat, VIC, Australia
Interests: power system planning and operation; forecasting in power systems; power system dynamics; data mining and artificial intelligence applications in power systems.
School of Engineering, RMIT University, Melbourne, Australia
Interests: operation of power networks with high renewable generation; power system dynamics and stability; power system planning and economics; power system reliability; load modelling and demand management; uncertainty modelling

Special Issue Information

Dear Colleagues,

The 33rd Australasian Universities Power Engineering Conference (AUPEC2023) was held from the 25th to 27th of September 2023 in Ballarat, Australia. The conference aimed to create a knowledge-sharing platform for researchers and industry professionals to present and discuss contemporary and emerging technologies. The conference invited researchers and industry professionals to submit novel and original ideas on various aspects of power and energy engineering; these included, but were not limited to, the following topics in the energy field:

  • Computer-aided analysis of power systems;
  • Condition monitoring and Fault diagnosis in power systems;
  • Cyber security of power systems and smart grids;
  • Data analytics in smart grids;
  • Generation and transmission system;
  • Planning and operation of energy systems;
  • Distribution system planning and operation;
  • Distributed energy resources;
  • DC-powered dwellings;
  • Electrical machines and drives;
  • Electricity market and economics;
  • Energy trading;
  • E-Mobility, electric vehicles, and charging systems (V2G, V2H, and G2V);
  • Energy management systems and SCADA systems;
  • Energy storage systems and technologies;
  • FACTS and HVDC;
  • Power electronics converters;
  • Power quality and harmonics;
  • Intelligent control of power systems;
  • Remote grids;
  • Off-shore/on-shore wind system;
  • Solar generation;
  • IoT and IoE in energy systems
  • Load and frequency control of energy systems;
  • Demand side management of energy systems;
  • Operation and planning of microgrids (DC, AC, and Hybrid);
  • Mine electrification;
  • Multi-agent frameworks for power systems;
  • Hydrogen network;
  • Power engineering education;
  • Power system protection;
  • Power system communication;
  • Power system stability and control;
  • Resiliency of power grids;
  • Smart grids, cities, and buildings;
  • Virtual power plants;
  • Multi-carrier energy systems;
  • Energy hubs and micro-energy hubs.

Prof. Dr. Nima Amjady
Dr. Kazi Hasan
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.


  • demand response
  • electric vehicles
  • electrification of heating and cooling
  • grid-forming control and stability
  • green hydrogen
  • hybrid energy storage

Published Papers (1 paper)

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12 pages, 2717 KiB  
A Model-Free Deep Reinforcement Learning-Based Approach for Assessment of Real-Time PV Hosting Capacity
by Jude Suchithra, Duane A. Robinson and Amin Rajabi
Energies 2024, 17(9), 2075; https://doi.org/10.3390/en17092075 - 26 Apr 2024
Viewed by 467
Assessments of the hosting capacity of electricity distribution networks are of paramount importance, as they facilitate the seamless integration of rooftop photovoltaic systems into the grid, accelerating the transition towards a more carbon neutral and sustainable system. This paper employs a deep reinforcement [...] Read more.
Assessments of the hosting capacity of electricity distribution networks are of paramount importance, as they facilitate the seamless integration of rooftop photovoltaic systems into the grid, accelerating the transition towards a more carbon neutral and sustainable system. This paper employs a deep reinforcement learning-based approach to evaluate the real-time hosting capacity of low voltage distribution networks in a model-free manner. The proposed approach only requires real-time customer voltage data and solar irradiation data to provide a fast and accurate estimate of real-time hosting capacity at each customer connection point. This study addresses the imperative for accurate electrical models, which are frequently unavailable, in evaluating the hosting capacity of electricity distribution networks. To meet this challenge, the proposed approach utilizes a deep neural network-based, data-driven model of a low-voltage electricity distribution network. This proposed methodology incorporates model-free elements, enhancing its adaptability and robustness. In addition, a comparative analysis between model-based and model-free hosting capacity assessment methods is presented, highlighting their respective strengths and weaknesses. The utilization of the proposed hosting capacity estimation model enables distribution network service providers to make well-informed decisions regarding grid planning, leading to cost minimization. Full article
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