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Renewable Energy Sources for Electrical Power: Reliability Assessment, Condition Monitoring, Prognostics and Health Management, Production Prediction

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

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 18990

Special Issue Editors

Dr. Sameer Al-Dahidi

E-Mail Website
Guest Editor
Department of Mechanical and Maintenance Engineering, School of Applied Technical Sciences, German Jordanian University, Amman 11180, Jordan
Interests: prognostics and health management; predictive maintenance; RAMS; artificial intelligence; machine learning; data mining; optimization; mathematical modelling; renewable energy; wind and solar photovoltaic systems; energy forecasting; performance analysis; mechanical engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Mohamed Louzazni

E-Mail Website
Guest Editor
Science Engineer Laboratory for Energy, National School of Applied Sciences, Chouaib Doukkali University of El Jadida, El Jadida P.O. Box 20, Morocco
Interests: performance analysis; monitoring; lifetime analysis; fault detection; control management; hybrid renewable energy; mathematical modelling; optimization and meta-heuristic algorithm; computational intelligence; photovoltaic & power energy; forecasting; fuel cell; radar; radio frequency; electromagnetic and electronic
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Enrico Zio

E-Mail Website
Guest Editor
1. Centre de Recherce sur les Risques et les Crises (CRC), MINES Paris-PSL Université, 06904 Paris, France
2. Energy Department, Politecnico di Milano, Via La Masa 34, 20156 Milano, Italy
Interests: risk assessment; reliability analysis; simulation; artificial intelligence; nuclear systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Renewable energy sources for electric power generation have been rapidly developing. Their integration into smart power grids aims at targets of energy efficiency with low-carbon operations and energy security. Significant challenges occur in the integration of renewable energy sources at both high and low voltage levels. Additionally, various conditions are required to ensure the safety and reliability of electric power systems based on renewable sources, including wind turbines, photovoltaic power systems, and so on. In this regard, reliability assessment, advanced condition monitoring, early fault detection, fault diagnostics, fault prognostics, system health management, and accurate production prediction under variable weather conditions are of paramount importance.

This Special Issue aims to encourage researchers and practitioners to share and exchange their original and high-quality articles (new theories, methods, techniques, and applications) in the fields of electrical power generation, transmission and distribution, and the integration of renewable energy systems as related to the aforementioned topics. In particular, potential topics include but are not limited to: reliability analysis; reliability testing and statistics; advanced condition monitoring; fault tolerance control; advanced fault detection, diagnostics, and prognostics; prognostics and health management; degradation modeling and analysis; failure mechanisms; short, intermediate, and long-term power production prediction; and embedded systems and the Internet of Things (IoT). The submitted manuscripts for this Special Issue will be peer-reviewed before publication.

Dr. Sameer Al-Dahidi
Dr. Mohamed Louzazni
Prof. Dr. Enrico Zio
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

  • renewable energy systems
  • performance analysis
  • reliability assessment
  • optimization
  • condition monitoring
  • prognostics and health management
  • production prediction

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Published Papers (4 papers)

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Research

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20 pages, 7280 KiB  
Article
Evaluation of Optimal Occasional Tilt on Photovoltaic Power Plant Energy Efficiency and Land Use Requirements, Iran
by Amirhossein Fathi, Masoomeh Bararzadeh Ledari and Yadollah Saboohi
Sustainability 2021, 13(18), 10213; https://doi.org/10.3390/su131810213 - 13 Sep 2021
Cited by 4 | Viewed by 1907
Abstract
The paper studies the optimum panel horizontal orientation angle toward the Sun and the optimum time interval of the panel’s movement. The optimum time intervals or panel movement can change the rate of input energy to the panel surface in Iran. For this [...] Read more.
The paper studies the optimum panel horizontal orientation angle toward the Sun and the optimum time interval of the panel’s movement. The optimum time intervals or panel movement can change the rate of input energy to the panel surface in Iran. For this purpose, a neural network has been trained to estimate the intensity of solar radiation in Iran. After model validation, the intensity of solar radiation has been estimated by selecting adequate geographical regions. Based on the intensity of sunlight, Iran has been divided into ten regions. In these regions, 40 cities have been randomly selected to study the effect of the panel’s angle variations within appropriate time intervals, as well as equal time intervals. The results show that the choice of the mounting system with the possibility of five angles’ implementation can increase the amount of solar energy between 3.9% and 7.4%. Compared to this number of angles at the equal time intervals, the amount of incoming solar energy has increased by 3% to 7%. In the first and second cases, the area of the power plant increases by about 12% to 24% compared to the yearly optimum tilt angle. Moreover, the amount of radiation incoming to the panel with the optimum operating angle is in alignment with the results of PVsyst software. Full article
(This article belongs to the Special Issue Renewable Energy Sources for Electrical Power: Reliability Assessment, Condition Monitoring, Prognostics and Health Management, Production Prediction)
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20 pages, 1692 KiB  
Article
Peruvian Electrical Distribution Firms’ Efficiency Revisited: A Two-Stage Data Envelopment Analysis
by Raúl Pérez-Reyes and Beatriz Tovar
Sustainability 2021, 13(18), 10066; https://doi.org/10.3390/su131810066 - 8 Sep 2021
Cited by 2 | Viewed by 2464
Abstract
The extent to which the structural reform of the Peruvian electricity market in the 1990s has improved the technical efficiency levels of the distribution companies and whether some firm specific explanatory variables had influenced upon the efficiency was analysed for first time using [...] Read more.
The extent to which the structural reform of the Peruvian electricity market in the 1990s has improved the technical efficiency levels of the distribution companies and whether some firm specific explanatory variables had influenced upon the efficiency was analysed for first time using a second stage Tobit model to study the influence of some firm specific explanatory variables on efficiency. Some authors have argued that the use of Tobit regression is inappropriate in the second stage of DEA and have suggested using other recently developed options. Due to this, it might be worth revisiting this issue and adding those other alternative models to check whether the conclusions obtained with the Tobit model could be upheld. The nine alternative models estimated allow us to confirm that the incentives generated by the reform process led to the firms becoming more efficient. Moreover, private management and the ratio of low voltage sales to medium voltage sales for each company positively affect efficiency, whereas investment per customer is negatively correlated to it. Full article
(This article belongs to the Special Issue Renewable Energy Sources for Electrical Power: Reliability Assessment, Condition Monitoring, Prognostics and Health Management, Production Prediction)
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19 pages, 4383 KiB  
Article
Bootstrapped Ensemble of Artificial Neural Networks Technique for Quantifying Uncertainty in Prediction of Wind Energy Production
by Sameer Al-Dahidi, Piero Baraldi, Enrico Zio and Lorenzo Montelatici
Sustainability 2021, 13(11), 6417; https://doi.org/10.3390/su13116417 - 4 Jun 2021
Cited by 8 | Viewed by 3332
Abstract
The accurate prediction of wind energy production is crucial for an affordable and reliable power supply to consumers. Prediction models are used as decision-aid tools for electric grid operators to dynamically balance the energy production provided by a pool of diverse sources in [...] Read more.
The accurate prediction of wind energy production is crucial for an affordable and reliable power supply to consumers. Prediction models are used as decision-aid tools for electric grid operators to dynamically balance the energy production provided by a pool of diverse sources in the energy mix. However, different sources of uncertainty affect the predictions, providing the decision-makers with non-accurate and possibly misleading information for grid operation. In this regard, this work aims to quantify the possible sources of uncertainty that affect the predictions of wind energy production provided by an ensemble of Artificial Neural Network (ANN) models. The proposed Bootstrap (BS) technique for uncertainty quantification relies on estimating Prediction Intervals (PIs) for a predefined confidence level. The capability of the proposed BS technique is verified, considering a 34 MW wind plant located in Italy. The obtained results show that the BS technique provides a more satisfactory quantification of the uncertainty of wind energy predictions than that of a technique adopted by the wind plant owner and the Mean-Variance Estimation (MVE) technique of literature. The PIs obtained by the BS technique are also analyzed in terms of different weather conditions experienced by the wind plant and time horizons of prediction. Full article
(This article belongs to the Special Issue Renewable Energy Sources for Electrical Power: Reliability Assessment, Condition Monitoring, Prognostics and Health Management, Production Prediction)
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Review

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22 pages, 2176 KiB  
Review
Business Model of Peer-to-Peer Energy Trading: A Review of Literature
by Hani Muhsen, Adib Allahham, Ala’aldeen Al-Halhouli, Mohammed Al-Mahmodi, Asma Alkhraibat and Musab Hamdan
Sustainability 2022, 14(3), 1616; https://doi.org/10.3390/su14031616 - 29 Jan 2022
Cited by 46 | Viewed by 9538
Abstract
Peer-to-peer (P2P) energy trading is a promising energy trading mechanism due to the deployment of distributed energy resources in recent years. Trading energy between prosumers and consumers in the local energy market is undergoing massive research and development, paying significant attention to the [...] Read more.
Peer-to-peer (P2P) energy trading is a promising energy trading mechanism due to the deployment of distributed energy resources in recent years. Trading energy between prosumers and consumers in the local energy market is undergoing massive research and development, paying significant attention to the business model of the energy market. In this paper, an extensive review was conducted on the current research in P2P energy trading to understand the business layer of the energy market concerning business model dimensions: bidding strategies and the market-clearing approach. Different types of game theoretical-based and auction-based market-clearing mechanisms are investigated, including a detailed classification of auctions. This study considers the possibility of employing the P2P technique in developing countries and reviewing existing business models and trading policies. The business layer of the P2P structure plays a vital role in developing an effective trading mechanism based on interactive energy markets. Full article
(This article belongs to the Special Issue Renewable Energy Sources for Electrical Power: Reliability Assessment, Condition Monitoring, Prognostics and Health Management, Production Prediction)
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