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Sustainable Energy Management: Research in Technology, Economics, and Policy
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Sustainable Energy Management: Research in Technology, Economics, and Policy

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

Deadline for manuscript submissions: closed (1 March 2023) | Viewed by 6265

Special Issue Editors

Dr. Yusen He

E-Mail Website
Guest Editor
Department of Industrial and System Engineering, University of Iowa, Iowa City, IA 52246, USA
Interests: deep learning; statistical modeling; image analysis; time-series analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Xun Shen

E-Mail Website
Guest Editor
Department of System and Control Engineering, Tokyo Institute of Technology, Tokyo, Japan
Interests: automotive control; intelligent driving system; chance constrained optimization; computation; statistics
Special Issues, Collections and Topics in MDPI journals
Dr. Tinghui Ouyang

E-Mail Website
Guest Editor
Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), 2 Chome-3-26 Aomi, Koto City, Tokyo 135-0064, Japan
Interests: machine learning; data mining; anomaly detection; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

According to the policy requirements on "dual-carbon" around the world, sustainable energy has been an important factor attracting the attention of both governments and researchers again. It is well-known that the development of sustainable energy in the past decades has achieved great success, and has been bringing the development of the national economy and affecting policy adjustment. In the background of “dual-carbon”, more new challenges appear with the development of big data and deep learning algorithms. For example, as large amounts of data in sustainable energy systems become easily available, how to clean unnecessary data, remove abnormal data, and detect unhealthy status and faults is pretty important to guarantee energy systems’ stability, security, and economy. With the development of artificial intelligence (AI), especially machine learning and deep learning techniques, many promising results have been achieved in sustainable energy systems, and how to leverage these techniques in advanced condition monitoring, energy management and policy-making is challengeable.

With the above considerations, this Special Issue aims at collecting papers on many challenging problems for the sustainability of renewable energy development, anomaly detection and condition monitoring related to sustainable energy systems, data sciences and artificial intelligence techniques in energy management, and some possible advanced policy.

We would like to invite both methodological and practical research on sustainable energy management related to the above topics. Following the high standards of the Sustainability journal, the relevant topics for this Special Issue could include but not limited to these ones:

  • Wind energy system control and management
  • Solar energy prediction and management
  • Hydropower conversion and management
  • Sustainable energy generation and control
  • Carbon reduction assessment from sustainable energy systems
  • Applications of AI, deep learning, and other data-science related tools for managing renewable energy systems
  • Smart grid control and condition monitoring
  • Anomaly detection in renewable energy systems
  • Policy review in energy management
  • Biofuel and bioenergy management

Dr. Yusen He
Dr. Xun Shen
Dr. Tinghui Ouyang
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

  • sustainable energy
  • anomaly detection
  • condition monitoring data sciences
  • machine learning
  • deep learning

Published Papers (4 papers)

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Research

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18 pages, 4182 KiB  
Article
Research on Real-Time Prediction of Hydrogen Sulfide Leakage Diffusion Concentration of New Energy Based on Machine Learning
by Xu Tang, Dali Wu, Sanming Wang and Xuhai Pan
Sustainability 2023, 15(9), 7237; https://doi.org/10.3390/su15097237 - 26 Apr 2023
Cited by 2 | Viewed by 1367
Abstract
China’s sour gas reservoir is very rich in reserves, taking the largest whole offshore natural gas field in China-Puguang gas field as an example, its hydrogen sulfide content reaches 14.1%. The use of renewable energy, such as solar energy through photocatalytic technology, can [...] Read more.
China’s sour gas reservoir is very rich in reserves, taking the largest whole offshore natural gas field in China-Puguang gas field as an example, its hydrogen sulfide content reaches 14.1%. The use of renewable energy, such as solar energy through photocatalytic technology, can decompose hydrogen sulfide into hydrogen and monomeric sulfur, thus realizing the conversion and resourceization of hydrogen sulfide gas, which has important research value. In this study, a concentration sample database of a hydrogen sulfide leakage scenario in a chemical park is constructed by Fluent software simulation, and then a leakage concentration prediction model is constructed based on the data samples to predict the hydrogen sulfide leakage diffusion concentration in real-time. Several machine learning algorithms, such as neural networks, support vector machines, and deep confidence networks, are implemented and compared to find the model algorithm with the best prediction performance. The prediction performance of the support vector machine model optimized by the sparrow search algorithm is found to be the best. The prediction model ensures the accuracy of the prediction results while greatly reducing the computational time cost, and the accuracy meets the requirements of practical engineering applications. Full article
(This article belongs to the Special Issue Sustainable Energy Management: Research in Technology, Economics, and Policy)
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16 pages, 3111 KiB  
Article
Policy Recommendations for Distributed Solar PV Aiming for a Carbon-Neutral Future
by Jiehui Yuan, Wenli Yuan, Juan Yuan, Zhihong Liu, Jia Liao and Xunmin Ou
Sustainability 2023, 15(4), 3005; https://doi.org/10.3390/su15043005 - 07 Feb 2023
Cited by 2 | Viewed by 1176
Abstract
Distributed-solar-photovoltaic (PV) generation is a key component of a new energy system aimed at carbon peaking and carbon neutrality. This paper establishes a policy-analysis framework for distributed-solar-PV generation based on a technical- and economic-evaluation model. Given that the resource endowment is becoming lower [...] Read more.
Distributed-solar-photovoltaic (PV) generation is a key component of a new energy system aimed at carbon peaking and carbon neutrality. This paper establishes a policy-analysis framework for distributed-solar-PV generation based on a technical- and economic-evaluation model. Given that the resource endowment is becoming lower and the raw material costs are becoming higher, the profitability of the deployment of distributed-solar-PV-generation projects in China is generally becoming much worse. Some distributed-PV-generation projects are even becoming unprofitable. This will not be helpful for the sustainable development of distributed-PV generation, which will play a vital role in attaining the goal of carbon neutrality. Based on the established model for techno-economic evaluation, a systematic policy analysis is performed to identify the effect of possible policy instruments such as financial policies on improving the economic profitability of distributed-PV-development in China. The results indicate that policy instruments related to preferential financing, green certificate, tax incentives and combinations thereof are available for priority measures aimed at optimizing incentive policies for enhancing the economic viability of distributed-PV deployment in China. Based on these findings, recommendations are proposed to optimize the currently available policy instruments for accelerating the sustainable development of the distributed-PV industry towards a carbon-neutral future. Full article
(This article belongs to the Special Issue Sustainable Energy Management: Research in Technology, Economics, and Policy)
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14 pages, 7235 KiB  
Article
Study on Critical Factors Affecting Tidal Current Energy Exploitation in the Guishan Channel Area of Zhoushan
by Zhou Ye, Wenwei Gu and Qiyan Ji
Sustainability 2022, 14(24), 16820; https://doi.org/10.3390/su142416820 - 15 Dec 2022
Viewed by 1203
Abstract
As a new type of clean and renewable energy, tidal current energy has attracted more and more attention from scholars. The Zhoushan Guishan Channel area (GCA) is an important part of the East China Sea port area, with strong currents due to its [...] Read more.
As a new type of clean and renewable energy, tidal current energy has attracted more and more attention from scholars. The Zhoushan Guishan Channel area (GCA) is an important part of the East China Sea port area, with strong currents due to its special terrain. In order to more comprehensively evaluate the characteristics of tidal energy development near the GCA, this paper uses the MIKE21 FM hydrodynamic model to simulate the tidal hydrodynamic process in the Zhoushan sea area and verifies the reliability of the model through the measured data. Based on the results of numerical simulations, the energy flow density, frequency of flow rate occurrence, flow asymmetry, flow rotation, and effective flow time that can be exploited are considered as the key factors affecting the development of tidal current energy. The distribution characteristics of each influencing factor in the region and the different influences on tidal current energy development are analyzed. Numerical simulations show that the average high-tide velocity in the GCA is lower than the ebb-tide velocity, and the duration of the high tide is also shorter than that of the ebb tide, which has a higher flow velocity than the surrounding area. The annual average energy flow density in the GCA is the highest at 4520 W/m2, and the spatial distribution is uneven. The resource level in the central part is much higher than that at both ends of the waterway. Three sections, i.e., A-A′, B-B′, and C-C′, with different key influence factors are selected for specific analysis, and it is concluded that the tidal energy development conditions are relatively superior near the B-B’ section in the middle of the GCA, and the exploitable power calculated using the Flux method is about 24.19 MW. The discussion of the results provides a certain reference for the development of local tidal current energy. These key factors affecting tidal current energy development can also be applied to assess the suitability of tidal current energy resource development in other regions. Full article
(This article belongs to the Special Issue Sustainable Energy Management: Research in Technology, Economics, and Policy)
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Review

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17 pages, 958 KiB  
Review
Review of Energy Portfolio Optimization in Energy Markets Considering Flexibility of Power-to-X
by Nicolai Lystbæk, Mikkel Gregersen and Hamid Reza Shaker
Sustainability 2023, 15(5), 4422; https://doi.org/10.3390/su15054422 - 01 Mar 2023
Viewed by 1750
Abstract
Power-to-X is one of the most attention-grabbing topics in the energy sector. Researchers are exploring the potential of harnessing power from renewable technologies and converting it into fuels used in various industries and the transportation sector. With the current market and research emphasis [...] Read more.
Power-to-X is one of the most attention-grabbing topics in the energy sector. Researchers are exploring the potential of harnessing power from renewable technologies and converting it into fuels used in various industries and the transportation sector. With the current market and research emphasis on Power-to-X and the accompanying substantial investments, a review of Power-to-X is becoming essential. Optimization will be a crucial aspect of managing an energy portfolio that includes Power-to-X and electrolysis systems, as the electrolyzer can participate in multiple markets. Based on the current literature and published reviews, none of them adequately showcase the state-of-the-art optimization algorithms for energy portfolios focusing on Power-to-X. Therefore, this paper provides an in-depth review of the optimization algorithms applied to energy portfolios with a specific emphasis on Power-to-X, aiming to uncover the current state-of-the-art in the field. Full article
(This article belongs to the Special Issue Sustainable Energy Management: Research in Technology, Economics, and Policy)
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