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Sustainable Energy Systems: From Grid Efficiency Through AI-Based Solutions to Energy Communities

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 2694

Special Issue Editors

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Guest Editor
Computer Science Department, College of Engineering, Effat University, Jeddah, Saudi Arabia
Interests: cognitive computing; artificial intelligence; data science; bioinformatics; innovation; big data research; data mining; emerging technologies; information systems; technology driven innovation; knowledge management; semantic web
Special Issues, Collections and Topics in MDPI journals

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Guest Editor

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Guest Editor
1. School of Business & Economics, Deree—The American College of Greece, 6 Gravias Street, GR-153 42 Aghia Paraskevi, Athens, Greece
2. Effat College of Business, Effat University, Jeddah, Saudi Arabia
Interests: smart cities; migration; innovation networks; international business; political economy; economic integration; politics; EU; Central Europe; China
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Global warming is one of the top global challenging issues that must be addressed. Today’s smart city era facilitates the migration of traditional electric grids to smart grids. The smart grid infrastructure, attached to massive numbers of sensors, is scalable and flexible to collect and store data, which is further transformed and analyzed to provide valuable insights into energy efficiency via artificial intelligence techniques.

With the rapid development of computing infrastructure, tools, and techniques such as cloud computing, fog computing, edge computing, graphics processing units, and deep learning, the performance of many smart grid applications could be further optimized. In general, we can move forward in smart grids from two different points of view:

(i) Electric utility: How to introduce more green energy? How to reduce the electricity wastage between generated and actual consumed electricity? How to predict the energy demand (including peak demand) accurately? Is there a recommendation on optimal operation management?

(ii) End users: What is the energy usage profile of each electric appliance? How to reduce energy usage in power-hungry electric appliances?

This Special Issue will report high-quality research on artificial intelligence in the fields of smart grid, and, more specifically, on state-of-the-art approaches, methodologies, and systems for the design, development, deployment, and innovative use of these convergence technologies for providing insights towards energy science and engineering. Key topics include but are not limited to the following:

  • New theories and applications of artificial intelligence in smart grids;
  • Multiobjective optimization in computationally expensive optimization problems;
  • Multiobjective optimization in bi-level optimization problems;
  • Energy disaggregation techniques in non-intrusive load monitoring;
  • Smart grids for smart energy management in smart cities;
  • Migration of traditional electric grids to smart grids;
  • Modelling and simulation (or co-simulation) in smart grids;
  • Demand response in smart grids;
  • Smart grid solutions for rural areas, that is, smart transmission grids;
  • Modelling and simulation (or co-simulation) in smart grids;
  • Internet of Things and smart grids;
  • Fraud detection and predictive maintenance;
  • Demand response in smart grids;
  • Peak load management approaches in smart grids;
  • Cloud, fog, edge computing techniques in smart grids;
  • Smart grid solutions for rural areas;
  • Artificial intelligence policy challenges and management;
  • Social research in smart grids;
  • Case studies and pilot studies;
  • Evidence-based policy-making in the domain of smart grids in rural and urban contexts.

Prof. Miltiadis D. Lytras
Dr. Kwok Tai Chui
Prof. Anna Visvizi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at 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. Sustainability 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 2400 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.


  • artificial intelligence
  • energy efficiency
  • machine intelligence
  • machine learning
  • optimization algorithms
  • smart cities
  • smart grids
  • grid efficiency optimization: conventional and AI-enhanced methods
  • energy communities
  • prosumers
  • new "renewables" and the EU, including nuclear energy

Published Papers (1 paper)

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18 pages, 4349 KiB  
Control Method of Buses and Lines Using Reinforcement Learning for Short Circuit Current Reduction
by Sangwook Han
Sustainability 2020, 12(22), 9333; - 10 Nov 2020
Cited by 4 | Viewed by 1723
This paper proposes a reinforcement learning-based approach that optimises bus and line control methods to solve the problem of short circuit currents in power systems. Expansion of power grids leads to concentrated power output and more lines for large-scale transmission, thereby increasing short [...] Read more.
This paper proposes a reinforcement learning-based approach that optimises bus and line control methods to solve the problem of short circuit currents in power systems. Expansion of power grids leads to concentrated power output and more lines for large-scale transmission, thereby increasing short circuit currents. The short circuit currents must be managed systematically by controlling the buses and lines such as separating, merging, and moving a bus, line, or transformer. However, there are countless possible control schemes in an actual grid. Moreover, to ensure compliance with power system reliability standards, no bus should exceed breaker capacity nor should lines or transformers be overloaded. For this reason, examining and selecting a plan requires extensive time and effort. To solve these problems, this paper introduces reinforcement learning to optimise control methods. By providing appropriate rewards for each control action, a policy is set, and the optimal control method is obtained through a maximising value method. In addition, a technique is presented that systematically defines the bus and line separation measures, limits the range of measures to those with actual power grid applicability, and reduces the optimisation time while increasing the convergence probability and enabling use in actual power grid operation. In the future, this technique will contribute significantly to establishing power grid operation plans based on short circuit currents. Full article
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