Special Issue "Sustainable Technologies and Developments for Future Energy Systems"

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

Deadline for manuscript submissions: 15 November 2021.

Special Issue Editors

Prof. Dr. Dinh Hoa Nguyen
E-Mail Website
Guest Editor
International Institute for Carbon-Neutral Energy Research (WPI-I2CNER), and Institute of Mathematics for Industry (IMI), Kyushu University, Motooka 744, Japan
Interests: low-carbon; decentralized and autonous energy systems; smart grid; network systems; artificial intelligence; optimization; control systems
Prof. Dr. Hung Dinh Nguyen
E-Mail Website
Guest Editor
School of Electrical and Electronic Engineering, NTU, 50 Nanyang Avenue, Singapore
Interests: digital twinning; AI & machine learning applications in complex physical systems; electric power system analysis, optimization, operation, and control; security and stability assessment; emergency control for preventing blackouts; nonlinear dynamical systems; renewables integration
Prof. Dr. Javad Khazaei
E-Mail Website
Guest Editor
1. Department of Electrical Engineering, Penn State Harrisburg, Middletown, PA 17057, USA
2. Department of Architectural Engineering, Penn State University Park, PA 16802, USA
Interests: smart grid optimization; cybersecurity of cyber–physical power systems; microgrids operation and control
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Recent paradigm shifts have posed grand challenges to energy systems and have significantly changed their structures, operation and management. One of such major challenges is an urgent call for the decarbonization of energy systems, leading to extensive efforts towards the massive integration of renewable energy sources, the electrification of related infrastructures and industry sectors, and so on. The high-level penetration of renewables naturally introduces a great deal of intermittency and uncertainties that may compromise the reliable operation of the power grids. Another challenge is the structure of energy systems, which have been becoming increasingly complex with their intensified interdependency with other critical infrastructures, such as communication, transportation, gas systems, and the unprecedented level of integration of non-traditional components, including intermittent renewable resources, electric vehicles, electronic-based devices, and information data centers. Economic feasibility is also a bottleneck for implementing many interesting and promising technologies in realistic energy systems. Above all, the recent COVID-19 pandemic unexpectedly altered human living and working conditions, and upturned globalization and worldwide urbanization. The pandemic creates a new normal for society and economics, as well as energy demands and supply patterns. Energy system planning and management therefore need to consider such pandemic-induced changes.

At the same time, many innovative technologies and systems have been developed for tackling those challenges and deriving clean, efficient, smart, and resilient energy systems. Examples include artificial intelligence (AI), Internet of Things (IoT), smart devices and services, distributed ledger technologies (DLTs), etc., to name a few. Therefore, to cope with the aforementioned environmental, societal, and economic challenges of energy systems, while exploring the opportunities of recent advances in science and technology, this Special Issue aims to serve as a platform for energy researchers to present their recent works that contribute to deriving sustainable future energy systems. Both theoretical and practical research on technologies, analysis, and designs, which address the sustainability, complexity, efficiency, and resiliency of future energy systems, are welcome. Special attention will be given to studies on emerging technologies, such as machine learning and artificial intelligence, distributed ledger technologies, etc., for solving emergent challenges on the cybersecurity and resilience of energy systems under cyberattacks and natural disasters, which have not been accounted for in other Special Issues.

Prof. Dr. Dinh Hoa Nguyen
Prof. Dr. Hung Dinh Nguyen
Prof. Dr. Javad Khazaei
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 papers will be 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 1900 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

  • energy sustainability
  • energy security featuring pandemic responses
  • modeling and detection of cyberattacks
  • machine learning and artificial intelligence for energy systems
  • distributed ledger technologies for energy systems
  • renewable and distributed energy resources
  • carbon capture, storage and utilization considering urban metabolism
  • emerging energy technologies towards sustainability
  • energy efficiency
  • multi-energy systems (energy-transportation and power–thermal nexus)
  • energy systems modeling, optimization, and control (digital twining and IoT applications)
  • information and communication technologies for energy systems

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
Residential Energy Consumer Occupancy Prediction Based on Support Vector Machine
Sustainability 2021, 13(15), 8321; https://doi.org/10.3390/su13158321 - 26 Jul 2021
Viewed by 436
Abstract
The occupancy of residential energy consumers is an important subject to be studied to account for the changes on the load curve shape caused by paradigm shifts to consumer-centric energy markets or by significant energy demand variations due to pandemics, such as COVID-19. [...] Read more.
The occupancy of residential energy consumers is an important subject to be studied to account for the changes on the load curve shape caused by paradigm shifts to consumer-centric energy markets or by significant energy demand variations due to pandemics, such as COVID-19. For non-intrusive occupancy analysis, multiple types of sensors can be installed to collect data based on which the consumer occupancy can be learned. However, the overall system cost will be increased as a result. Therefore, this research proposes a cheap and lightweight machine learning approach to predict the energy consumer occupancy based solely on their electricity consumption data. The proposed approach employs a support vector machine (SVM), in which different kernels are used and compared, including positive semi-definite and conditionally positive definite kernels. Efficiency of the proposed approach is depicted by different performance indexes calculated on simulation results with a realistic, publicly available dataset. Among SVM models with different kernels, those with Gaussian (rbf) and sigmoid kernels have the highest performance indexes, hence they may be most suitable to be used for residential energy consumer occupancy prediction. Full article
(This article belongs to the Special Issue Sustainable Technologies and Developments for Future Energy Systems)
Show Figures

Figure 1

Article
State-Aware Stochastic Optimal Power Flow
Sustainability 2021, 13(14), 7577; https://doi.org/10.3390/su13147577 - 07 Jul 2021
Viewed by 410
Abstract
The increase in distributed generation (DG) and variable load mandates system operators to perform decision-making considering uncertainties. This paper introduces a novel state-aware stochastic optimal power flow (SA-SOPF) problem formulation. The proposed SA-SOPF has objective to find a day-ahead base-solution that minimizes the [...] Read more.
The increase in distributed generation (DG) and variable load mandates system operators to perform decision-making considering uncertainties. This paper introduces a novel state-aware stochastic optimal power flow (SA-SOPF) problem formulation. The proposed SA-SOPF has objective to find a day-ahead base-solution that minimizes the generation cost and expectation of deviations in generation and node voltage set-points during real-time operation. We formulate SA-SOPF for a given affine policy and employ Gaussian process learning to obtain a distributionally robust (DR) affine policy for generation and voltage set-point change in real-time. In simulations, the GP-based affine policy has shown distributional robustness over three different uncertainty distributions for IEEE 14-bus system. The results also depict that the proposed SA-OPF formulation can reduce the expectation in voltage and generation deviation more than 60% in real-time operation with an additional day-ahead scheduling cost of 4.68% only for 14-bus system. For, in a 30-bus system, the reduction in generation and voltage deviation, the expectation is achieved to be greater than 90% for 1.195% extra generation cost. These results are strong indicators of possibility of achieving the day-ahead solution which lead to lower real-time deviation with minimal cost increase. Full article
(This article belongs to the Special Issue Sustainable Technologies and Developments for Future Energy Systems)
Show Figures

Figure 1

Back to TopTop