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Sustainability
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Sustainability of Photovoltaics
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Sustainability of Photovoltaics

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 2477

Special Issue Editors

Dr. Francesco Riganti-Fulginei

E-Mail Website
Guest Editor
Department of Industrial, Electronic and Mechanical Engineering, Università degli Studi di Roma Tre, 00146 Roma, Italy
Interests: electric vehicle technologies; renewable energy systems; machines and drives; power electronics; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Simone Quondam-Antonio

E-Mail Website
Guest Editor
Department of Engineering, University of Perugia, Via G. Duranti 93, 06125 Perugia, Umbria, Italy
Interests: Magnetic measurements and experimental characterization; hysteresis modelling and simulation; characterization of magnetic components and devices for industry applications

Special Issue Information

Dear Colleagues,

Motivated by the need to depart from fossil fuel-based electricity, photovoltaic (PV) power generation continues to attract attention from universities, industries and governments. As the cost of solar panels manufacturing decreases, PV power generation has become competitive with other forms of renewable energy, and is being massively deployed. In this scenario, this Special Issue aims at producing original research in various aspects of photovoltaic energy devices as enabling technology for efficient utilization of PV power generation systems.

Topics of interest include, but are not limited to, the following:

  • Integrated PV cell-converter architectures;
  • DC-DC converter topologies for PV applications;
  • Maximum power point tracking algorithms;
  • Inverter topologies for PV applications;
  • Microgrid applications
  • Artificial intelligence for control and optimization of PV systems;
  • Inverter modulation and control for ancillary services (e.g., power quality)
  • Circuital models of PV arrays
  • Integrated PV power plants plus storage;
  • PV power plants for electric vehicle charging;
  • Storage systems for photovoltaic applications

Dr. Francesco Riganti-Fulginei
Dr. Simone Quondam-Antonio
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

  • Environment
  • Solar Energy
  • Photovoltaics
  • Renewable Energy
  • Green Building
  • Artificial Intelligence

Published Papers (1 paper)

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Research

16 pages, 3491 KiB  
Article
Parameter Extraction of Three Diode Solar Photovoltaic Model Using Improved Grey Wolf Optimizer
by Abd-ElHady Ramadan, Salah Kamel, Tahir Khurshaid, Seung-Ryle Oh and Sang-Bong Rhee
Sustainability 2021, 13(12), 6963; https://0-doi-org.brum.beds.ac.uk/10.3390/su13126963 - 21 Jun 2021
Cited by 29 | Viewed by 1956
Abstract
The enhancement of photovoltaic (PV) energy systems relies on an accurate PV model. Researchers have made significant efforts to extract PV parameters due to their nonlinear characteristics of the PV system, and the lake information from the manufactures’ PV system datasheet. PV parameters [...] Read more.
The enhancement of photovoltaic (PV) energy systems relies on an accurate PV model. Researchers have made significant efforts to extract PV parameters due to their nonlinear characteristics of the PV system, and the lake information from the manufactures’ PV system datasheet. PV parameters estimation using optimization algorithms is a challenging problem in which a wide range of research has been conducted. The idea behind this challenge is the selection of a proper PV model and algorithm to estimate the accurate parameters of this model. In this paper, a new application of the improved gray wolf optimizer (I-GWO) is proposed to estimate the parameters’ values that achieve an accurate PV three diode model (TDM) in a perfect and robust manner. The PV TDM is developed to represent the effect of grain boundaries and large leakage current in the PV system. I-GWO is developed with the aim of improving population, exploration and exploitation balance and convergence of the original GWO. The performance of I-GWO is compared with other well-known optimization algorithms. I-GWO is evaluated through two different applications. In the first application, the real data from RTC furnace is applied and in the second one, the real data of PTW polycrystalline PV panel is applied. The results are compared with different evaluation factors (root mean square error (RMSE), current absolute error and statistical analysis for multiple independent runs). I-GWO achieved the lowest RMSE values in comparison with other algorithms. The RMSE values for the two applications are 0.00098331 and 0.0024276, respectively. Based on quantitative and qualitative performance evaluation, it can be concluded that the estimated parameters of TDM by I-GWO are more accurate than those obtained by other studied optimization algorithms. Full article
(This article belongs to the Special Issue Sustainability of Photovoltaics)
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