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Artificial Intelligence (AI) for Smart Energy and Mobility

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: 20 December 2024 | Viewed by 1227

Special Issue Editors


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Guest Editor
Alliance Manchester Business School, The University of Manchester, Manchester, UK
Interests: energy data analytics; energy market with demand response and renewable energy integration; smart energy and mobility; machine learning; game theory and optimisation

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Guest Editor
Alliance Manchester Business School, The University of Manchester, Manchester, UK
Interests: artificial intelligence; optimization; real-world applications; simulation
Alliance Manchester Business School, The University of Manchester, Manchester, UK
Interests: decision science; data analytics; performance assessment of decentralised energy and renewable energy

Special Issue Information

Dear Colleagues,

The energy and transport sectors account for the majority of the total global emissions and are major barriers to achieving net-zero goals. This incentivises ongoing efforts on the development of smart, clean and modern energy technologies (e.g., demand response, microgrids, and renewable energy) on the one hand, and the increasing integration of smart mobility technologies such as electric vehicles (EVs) for road, sea, and aerial transport, smart charging and routing on the other hand. The co-development and integration of smart energy and smart mobility technologies in both energy and transport sectors will bring significant benefits; however, at the same time create unprecedented challenges to reliable and efficient system operations. For instance, with the uptake of EVs and charging infrastructures, electricity demand will increase significantly, which inevitably accelerates the growth of renewable energy to meet the demand and thus leads to increased pressures on existing energy system planning and operations. On the other hand, renewable energy, EV and charging infrastructure integration in energy systems, considering their sizing and location selection, will affect existing transport planning and operations (e.g., traditional- and petrol-station-dominated markets) and create new and challenging research agendas.

A coupled system involving energy and transport sectors exhibits complex interactive behaviours among different parties, which presents a series of challenging and open research questions to be answered. Artificial intelligence (AI), as an enabling technology, is a promising method and tool to facilitate the development of smart energy and mobility solutions, the digital transformation of both energy and transport sectors and ultimately, the achievement of net-zero targets. Therefore, this Special Issue is devoted to the latest developments in AI (e.g., machine learning, digital twin, optimisation and simulation) for Smart Energy and Mobility. Prospective authors are invited to submit original contributions that include but are not limited to the following topics of interest:

  • Innovative AI (e.g., machine learning, digital twin, optimisation and simulation) applications in energy and/ or transport sectors;
  • Performance and decision analysis in energy and/ or transport sectors;
  • Integrated energy and transport system modelling;
  • The impact of EVs on system operations of energy and/ or transport sectors;
  • The coordination of EVs and renewable energy integration;
  • Smart charging and operations of EVs;
  • EVs as demand response provider in smart grids;
  • EVs driving behaviour analysis;
  • EVs charging station sizing and location selection;
  • Game-theoretic modelling of EVs and renewable integration for energy and/ or transport sectors.

Technical surveys and review papers are highly encouraged for submission and possible publication in this Special Issue.

Dr. Fanlin Meng
Prof. Dr. Richard Allmendinger
Dr. Ting Wu
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

  • AI
  • smart energy
  • smart mobility
  • transport
  • EVs
  • machine learning
  • digital twin
  • simulation
  • optimisation
  • game theory
  • decision-making

Published Papers (1 paper)

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Research

19 pages, 4957 KiB  
Article
A Comparative Study of Optimal PV Allocation in a Distribution Network Using Evolutionary Algorithms
by Wenlei Bai, Wen Zhang, Richard Allmendinger, Innocent Enyekwe and Kwang Y. Lee
Energies 2024, 17(2), 511; https://doi.org/10.3390/en17020511 - 20 Jan 2024
Cited by 1 | Viewed by 745
Abstract
The growing distributed energy resource (DER) penetration into distribution networks, such as through residential and commercial photovoltaics (PV), has emerged through a transition from passive to active networks, which takes the complexity of planning and operations to the next level. Optimal PV allocation [...] Read more.
The growing distributed energy resource (DER) penetration into distribution networks, such as through residential and commercial photovoltaics (PV), has emerged through a transition from passive to active networks, which takes the complexity of planning and operations to the next level. Optimal PV allocation (sizing and location) is challenging because it involves mixed-integer non-linear programming with three-phase non-linear unbalanced power flow equations. Meta-heuristic algorithms have proven their effectiveness in many complex engineering problems. Thus, in this study, we propose to achieve optimal PV allocation by using several basic evolutionary algorithms (EAs), particle swarm optimization (PSO), artificial bee colony (ABC), differential evolution (DE), and their variants, all of which are applied for a study of their performance levels. Two modified unbalanced IEEE test feeders (13 and 37 bus) are developed to evaluate these performance levels, with two objectives: one is to maximize PV penetration, and the other is to minimize the voltage deviation from 1.0 p.u. To handle the computational burden of the sequential power flow and unbalanced network, we adopt an efficient iterative load flow algorithm instead of the commonly used and yet highly simplified forward–backward sweep method. A comparative study of these basic EAs shows their general success in finding a near-optimal solution, except in the case of the DE, which is known for solving continuous optimization problems efficiently. From experiments run 30 times, it is observed that PSO-related algorithms are more efficient and robust in the maximum PV penetration case, while ABC-related algorithms are more efficient and robust in the minimum voltage deviation case. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) for Smart Energy and Mobility)
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