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

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 18381

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Special Issue Editors

Prof. Dr. Dinh Hoa Nguyen

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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
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Hung Dinh Nguyen

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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

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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, Collections and Topics 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 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.

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 (10 papers)

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Research

35 pages, 10062 KiB

Open AccessArticle

A Particle Swarm Optimization–Adaptive Weighted Delay Velocity-Based Fast-Converging Maximum Power Point Tracking Algorithm for Solar PV Generation System

by Md Adil Azad, Mohd Tariq, Adil Sarwar, Injila Sajid, Shafiq Ahmad, Farhad Ilahi Bakhsh and Abdelaty Edrees Sayed

Sustainability 2023, 15(21), 15335; https://doi.org/10.3390/su152115335 - 26 Oct 2023

Cited by 5 | Viewed by 864

Abstract

Photovoltaic (PV) arrays have a considerably lower output when exposed to partial shadowing (PS). Whilst adding bypass diodes to the output reduces PS’s impact, this adjustment causes many output power peaks. Because of their tendency to converge to local maxima, traditional algorithms like perturb and observe and hill-climbing should not be used to track the optimal peak. The tracking of the optimal peak is achieved by employing a range of artificial intelligence methodologies, such as utilizing an artificial neural network and implementing control based on fuzzy logic principles. These algorithms perform satisfactorily under PS conditions but their training method necessitates a sizable quantity of data which result in placing an unnecessary demand on CPU memory. In order to achieve maximum power point tracking (MPPT) with fast convergence, minimal power fluctuations, and excellent stability, this paper introduces a novel optimization algorithm named PSO-AWDV (particle swarm optimization–adaptive weighted delay velocity). This algorithm employs a stochastic search approach, which involves the random exploration of the search space, to accomplish these goals. The efficacy of the proposed algorithm is demonstrated by conducting experiments on a series-connected configuration of four modules, under different levels of solar radiation. The algorithm successfully gets rid of the problems brought on by current traditional and AI-based methods. The PSO-AWDV algorithm stands out for its simplicity and reduced computational complexity when compared to traditional PSO and its variant PSO-VC, while excelling in locating the maximum power point (MPP) even in intricate shading scenarios, encompassing partial shading conditions and notable insolation fluctuations. Furthermore, its tracking efficiency surpasses that of both conventional PSO and PSO-VC. To further validate our results, we conducted a real-time hardware-in-the-loop (HIL) emulation, which confirmed the superiority of the PSO-AWDV algorithm over traditional and AI-based methods. Overall, the proposed algorithm offers a practical solution to the challenges of MPPT under PS conditions, with promising outcomes for real-world PV applications. Full article

(This article belongs to the Special Issue Sustainable Technologies and Developments for Future Energy Systems)

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22 pages, 12264 KiB

Open AccessArticle

Assessing the Optimal Contributions of Renewables and Carbon Capture and Storage toward Carbon Neutrality by 2050

by Dinh Hoa Nguyen, Andrew Chapman and Takeshi Tsuji

Sustainability 2023, 15(18), 13447; https://doi.org/10.3390/su151813447 - 07 Sep 2023

Cited by 1 | Viewed by 1214

Abstract

Building on the carbon reduction targets agreed in the Paris Agreements, many nations have renewed their efforts toward achieving carbon neutrality by the year 2050. In line with this ambitious goal, nations are seeking to understand the appropriate combination of technologies which will enable the required reductions in such a way that they are appealing to investors. Around the globe, solar and wind power lead in terms of renewable energy deployment, while carbon capture and storage (CCS) is scaling up toward making a significant contribution to deep carbon cuts. Using Japan as a case study nation, this research proposes a linear optimization modeling approach to identify the potential contributions of renewables and CCS toward maximizing carbon reduction and identifying their economic merits over time. Results identify that the combination of these three technologies could enable a carbon dioxide emission reduction of between 55 and 67 percent in the energy sector by 2050 depending on resilience levels and CCS deployment regimes. Further reductions are likely to emerge with increased carbon pricing over time. The findings provide insights for energy system design, energy policy making and investment in carbon reducing technologies which underpin significant carbon reductions, while identifying potential regional social co-benefits. Full article

(This article belongs to the Special Issue Sustainable Technologies and Developments for Future Energy Systems)

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18 pages, 2925 KiB

Open AccessArticle

Distributed Energy Resource Exploitation through Co-Optimization of Power System and Data Centers with Uncertainties during Demand Response

by Yu Weng, Yang Liu, Rachel Li Ting Lim and Hung D. Nguyen

Sustainability 2023, 15(14), 10995; https://doi.org/10.3390/su151410995 - 13 Jul 2023

Viewed by 747

Abstract

This paper presents a robust bi-level co-optimization model that promotes the active participation of Internet Data Centers (IDCs) in demand response (DR) programs, thereby enhancing the flexibility of power systems. Our approach involves leveraging virtual power lines to migrate workloads among IDCs, optimizing resource allocations, and benefiting both domains. The model incorporates a Gaussian Process Regression (GPR)-constructed DR price–amount curve, which largely contributes to the simplification of the optimization problem with high accuracy and computational efficiency. It also respects the information barriers between the two domains of power systems and IDCs, and thus safeguards the privacy and flexibility of IDCs. The uncertainty in IDC operations is considered by incorporating the variance in GPR into the demand response curve. By integrating IDCs as DR resources, the framework of this research enhances the flexibility of power systems and the efficiency of cross-domain co-optimization. The model and algorithm are validated using modified IEEE test systems. Full article

(This article belongs to the Special Issue Sustainable Technologies and Developments for Future Energy Systems)

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16 pages, 5788 KiB

Open AccessArticle

Optical Wireless Power Transfer for Implanted and Wearable Devices

by Dinh Hoa Nguyen

Sustainability 2023, 15(10), 8146; https://doi.org/10.3390/su15108146 - 17 May 2023

Cited by 1 | Viewed by 1487

Abstract

Optical wireless power transfer (OWPT) has been employed in the literature as a wireless powering approach for implanted and wearable devices. However, most of the existing studies on this topic have not studied the performances of OWPT systems when light is transmitted through clothing. This research therefore contributes to investigate the effects of clothing on OWPT performances from both theoretical and experimental perspectives. An obtained experimental result indicates that a single light-emitting diode (LED) transmitter is able to perform the OWPT through white cotton clothing, but failed with another dark cotton clothing, even at a small transmitting distance. Hence, this research proposes to employ LED arrays as optical transmitters to improve the OWPT system capability in terms of the wirelessly transmitted power, transmitting distance and system tolerance to misalignments, whilst keeping the system safety, low cost and simplicity. Consequently, a theoretical formula for the power transmission efficiency made by an LED array through clothing is proposed and then is verified with experimental results. Furthermore, the important role of multiple light reflections at the surfaces of clothing and the LED array transmitter is pointed out. Full article

(This article belongs to the Special Issue Sustainable Technologies and Developments for Future Energy Systems)

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16 pages, 2875 KiB

Open AccessArticle

A Modulation Method for Three-Phase Dual-Active-Bridge Converters in Battery Charging Applications

by Duy-Dinh Nguyen, The-Tiep Pham, Tat-Thang Le, Sewan Choi and Kazuto Yukita

Sustainability 2023, 15(6), 5170; https://doi.org/10.3390/su15065170 - 14 Mar 2023

Viewed by 2451

Abstract

The Three-phase Dual-Active-Bridge (DAB3) converters are a common choice for quick charging stations for batteries in electric vehicles due to their high power density, versatility, and galvanic isolation capability. However, the DAB3 topology has limited soft-switching range, particularly under light load conditions when the voltage conversion ratio differs significantly from unity, resulting in hard switching, increased loss, and higher electromagnetic interference. To address these issues, various techniques have been proposed, but they often lead to other problems such as higher current ripple or unbalanced thermal distribution. In this paper, a new modulation scheme, called symmetric duty-cycle control (SDM), is proposed for DAB3 converters to overcome these issues. A multiaspect comparison of SDM was conducted against two existing techniques, SPS and ADCC, and its superiority was validated through simulation and experimental results. Our proposed SDM scheme provides a current ripple within 10% to 15% of the average current and enables zero current switching for the whole voltage and power ranges. Additionally, a modified version of SDM can even improve overall efficiency by 7% compared to the conventional SPS technique. Full article

(This article belongs to the Special Issue Sustainable Technologies and Developments for Future Energy Systems)

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13 pages, 1963 KiB

Open AccessArticle

Renewable Energy Pathways toward Accelerating Hydrogen Fuel Production: Evidence from Global Hydrogen Modeling

by Shamal Chandra Karmaker, Andrew Chapman, Kanchan Kumar Sen, Shahadat Hosan and Bidyut Baran Saha

Sustainability 2023, 15(1), 588; https://doi.org/10.3390/su15010588 - 29 Dec 2022

Cited by 8 | Viewed by 2556

Abstract

Fossil fuel consumption has triggered worries about energy security and climate change; this has promoted hydrogen as a viable option to aid in decarbonizing global energy systems. Hydrogen could substitute for fossil fuels in the future due to the economic, political, and environmental concerns related to energy production using fossil fuels. However, currently, the majority of hydrogen is produced using fossil fuels, particularly natural gas, which is not a renewable source of energy. It is therefore crucial to increase the efforts to produce hydrogen from renewable sources, rather from the existing fossil-based approaches. Thus, this study investigates how renewable energy can accelerate the production of hydrogen fuel in the future under three hydrogen economy-related energy regimes, including nuclear restrictions, hydrogen, and city gas blending, and in the scenarios which consider the geographic distribution of carbon reduction targets. A random effects regression model has been utilized, employing panel data from a global energy system which optimizes for cost and carbon targets. The results of this study demonstrate that an increase in renewable energy sources has the potential to significantly accelerate the growth of future hydrogen production under all the considered policy regimes. The policy implications of this paper suggest that promoting renewable energy investments in line with a fairer allocation of carbon reduction efforts will help to ensure a future hydrogen economy which engenders a sustainable, low carbon society. Full article

(This article belongs to the Special Issue Sustainable Technologies and Developments for Future Energy Systems)

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26 pages, 6511 KiB

Open AccessArticle

Multi-Objective Optimal Operation of Building Energy Management Systems with Thermal and Battery Energy Storage in the Presence of Load Uncertainty

by Parichada Trairat and David Banjerdpongchai

Sustainability 2022, 14(19), 12717; https://doi.org/10.3390/su141912717 - 06 Oct 2022

Cited by 4 | Viewed by 1489

Abstract

This paper presents the optimal operation of a building energy management system (BEMS), with combined heat and power (CHP) generation, thermal energy storage (TES), and battery energy storage (BES), subject to load demand uncertainty. The main objective is to reduce the total operating cost (TOC) and total CO₂ emission (TCOE). First, we develop two models of load demand forecasting, one for weekday and the other for weekend, using artificial neural networks, long short-term memory, and convolutional neural networks. Then, we incorporate the predicted load demand and load demand uncertainty for planning the energy dispatch of the BEMS. TES aims to store the thermal energy waste from the power generation of CHP and discharge the thermal energy to the absorption chiller to supply the cooling load. BES and spinning reserve (SR) play an important role in handling the uncertainty of the load demand. The operation of BEMS, subject to the load demand uncertainty, is formulated as a linear program. We can efficiently solve the linear program and provide an optimal solution that satisfies the dispatch constraints. Thereafter, we determine the optimal size of BES, based on economics and environmental optimal operation. The proposed BEMS is compared to the previous BEMS, without BES and SR. Furthermore, we propose the multi-objective optimal operation, where the normalization for TOC and TCOE is introduced, and the multi-objective function is defined as a linear combination of normalized TOC and TCOE. The numerical results reveal the trade-off relationship between TOC and TCOE. In particular, when TCOE is minimum, TOC becomes maximum. On the other hand, when TOC is minimum, TCOE becomes maximum. The relationship provides a method to select the operating point, as well as analyze the power flow for the multi-objective optimal operation. Full article

(This article belongs to the Special Issue Sustainable Technologies and Developments for Future Energy Systems)

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18 pages, 3494 KiB

Open AccessArticle

Modeling and Economic Operation of Energy Hub Considering Energy Market Price and Demand

by Il-oh Kang, Hyunseok You, Kyungshik Choi, Sung-kook Jeon, Jaehee Lee and Dongho Lee

Sustainability 2022, 14(4), 2004; https://doi.org/10.3390/su14042004 - 10 Feb 2022

Cited by 2 | Viewed by 1492

Abstract

This paper discusses the economic operation strategy of the energy hub, which is being established in South Korea. The energy hub has five energy conversion devices: a turbo expander generator, a normal fuel cell, a fuel cell with a hydrogen outlet, a small-scale combined heat and power device, and a photovoltaic device. We are developing the most economically beneficial operation strategy for the operators who own the hub, without making any systematic improvements to the energy market. First, sixteen conversion efficiency matrices can be achieved by turning each device (except the PV) on or off. Next, even the same energy must be divided into different energy flows according to price. The energy flow is controlled to obtain the maximum profit, considering the internal load of the energy hub and the price fluctuations of the energy market. Using our operating strategy, the return on investment period is approximately 9.9 years, which is three years shorter than that without the operating strategy. Full article

(This article belongs to the Special Issue Sustainable Technologies and Developments for Future Energy Systems)

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13 pages, 1207 KiB

Open AccessArticle

Residential Energy Consumer Occupancy Prediction Based on Support Vector Machine

by Dinh Hoa Nguyen

Sustainability 2021, 13(15), 8321; https://doi.org/10.3390/su13158321 - 26 Jul 2021

Cited by 4 | Viewed by 1921

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. 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)

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16 pages, 443 KiB

Open AccessArticle

State-Aware Stochastic Optimal Power Flow

by Parikshit Pareek and Hung D. Nguyen

Sustainability 2021, 13(14), 7577; https://doi.org/10.3390/su13147577 - 07 Jul 2021

Cited by 2 | Viewed by 1681

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 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)

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