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Photovoltaic Power System: Modeling and Performance Analysis, 2nd Edition
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Photovoltaic Power System: Modeling and Performance Analysis, 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: closed (20 November 2024) | Viewed by 3896

Special Issue Editors

Prof. Dr. Manuela Sechilariu

E-Mail Website
Guest Editor
AVENUES, Université de Technologie de Compiègne, Centre Pierre Guillaumat-CS 60 319, 60203 Compiègne, France
Interests: energy management; microgrid; smart grid; photovoltaic
Special Issues, Collections and Topics in MDPI journals
Dr. Berk Celik

E-Mail Website
Guest Editor
Avenues, Centre Pierre Guillaumat, Université de Technologie de Compiègne, 60203 Compiègne, France
Interests: power system; renewable energy; energy storage; demand response
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, photovoltaic (PV) energy is a key element of the energy transition. In particular, the increasing share of PV production in the energy mix allows us to reduce greenhouse gas emissions. Recent PV developments, new technologies, and the increased efficiency of PV modules have led to a significant reduction in production costs. However, several challenges remain to be addressed in order to improve the reliability of power systems. Importantly, the grid integration of intermittent PV production must be addressed. This can be conducted by analyzing its various impacts, adding storage, providing a more accurate forecast of PV production, and/or considering uncertainties. Additionally, the assessment of economic and environmental impacts on the system lifecycle can ease PV development. Optimal sizing design, under optimal management and with optimal carbon impact, can improve PV system performances and lead to new architectures for implementing and/or controlling techniques. In addition, the experimental validation of these control techniques improves the reliability of the results and their integration into PV power systems.

In light of these growing trends, this Special Issue focuses on PV system modeling and performance analysis. The goal is to address current PV deployment challenges and bring new ideas, advances, and insights regarding PV power systems. Authors are invited to submit original contributions for review and possible publication. This Special Issue includes, but is not limited, the following topics:

  • Emerging PV technologies and state-of-the-art reviews on PV technologies (topologies, architectures, etc.);
  • MPPT methods (in particular for curved PV panels);
  • Forecasting of PV power (in particular for 3D solar irradiation models);
  • PV power system modeling including uncertainty mitigation;
  • Applications of PV systems (in particular, vehicle-integrated PV, PV-powered charging station, agri-PV, floating PV, PV energy communities, etc.):
    • Performance analysis of PV systems (energy efficiency, lifecycle economic and environmental impacts, etc.);
    • Control techniques, including cost and sizing optimization, as well as energy and real-time power management;
    • Grid integration of PV systems and ancillary services;
    • Case studies (real PV systems involving real measurements and/or experimental control technics methods validation).
  • Energy transition and reduced greenhouse gas emissions by increasing PV implementation (PV role in the energy mix, PV infrastructure designs, PV social impact, social acceptability and acceptance of PV infrastructures, etc.).

Prof. Dr. Manuela Sechilariu
Dr. Berk Celik
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. Applied Sciences 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

  • photovoltaic systems
  • MPPT energy efficiency
  • power electronic converters
  • environmental impact
  • power management
  • experimental testing
  • energy transition

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Published Papers (4 papers)

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Research

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19 pages, 4478 KiB  
Article
Novel Hybrid Optimization Technique for Solar Photovoltaic Output Prediction Using Improved Hippopotamus Algorithm
by Hongbin Wang, Nurulafiqah Nadzirah Binti Mansor and Hazlie Bin Mokhlis
Appl. Sci. 2024, 14(17), 7803; https://doi.org/10.3390/app14177803 - 3 Sep 2024
Cited by 1 | Viewed by 932
Abstract
This paper introduces a novel hybrid optimization technique aimed at improving the prediction accuracy of solar photovoltaic (PV) outputs using an Improved Hippopotamus Optimization Algorithm (IHO). The IHO enhances the traditional Hippopotamus Optimization (HO) algorithm by addressing its limitations in search efficiency, convergence [...] Read more.
This paper introduces a novel hybrid optimization technique aimed at improving the prediction accuracy of solar photovoltaic (PV) outputs using an Improved Hippopotamus Optimization Algorithm (IHO). The IHO enhances the traditional Hippopotamus Optimization (HO) algorithm by addressing its limitations in search efficiency, convergence speed, and global exploration. The IHO algorithm used Latin hypercube sampling (LHS) for population initialization, significantly enhancing the diversity and global search potential of the optimization process. The integration of the Jaya algorithm further refines solution quality and accelerates convergence. Additionally, a combination of unordered dimensional sampling, random crossover, and sequential mutation is employed to enhance the optimization process. The effectiveness of the proposed IHO is demonstrated through the optimization of weights and neuron thresholds in the extreme learning machine (ELM), a model known for its rapid learning capabilities but often affected by the randomness of initial parameters. The IHO-optimized ELM (IHO-ELM) is tested against benchmark algorithms, including BP, the traditional ELM, the HO-ELM, LCN, and LSTM, showing significant improvements in prediction accuracy and stability. Moreover, the IHO-ELM model is validated in a different region to assess its generalization ability for solar PV output prediction. The results confirm that the proposed hybrid approach not only improves prediction accuracy but also demonstrates robust generalization capabilities, making it a promising tool for predictive modeling in solar energy systems. Full article
(This article belongs to the Special Issue Photovoltaic Power System: Modeling and Performance Analysis, 2nd Edition)
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17 pages, 6836 KiB  
Article
Outdoor Performance Comparison of Bifacial and Monofacial Photovoltaic Modules in Temperate Climate and Industrial-like Rooftops
by Alejandro González-Moreno, Domenico Mazzeo, Alberto Dolara, Emanuele Ogliari and Sonia Leva
Appl. Sci. 2024, 14(13), 5714; https://doi.org/10.3390/app14135714 - 29 Jun 2024
Cited by 1 | Viewed by 1201
Abstract
To fully exploit the advantages of bifacial PV (bPV) modules and understand their performance under real-world conditions, a comprehensive investigation was conducted. It was focused on bPV installations with some mounting constraints, as in industrial rooftops, where the ideal high module-to-ground height for [...] Read more.
To fully exploit the advantages of bifacial PV (bPV) modules and understand their performance under real-world conditions, a comprehensive investigation was conducted. It was focused on bPV installations with some mounting constraints, as in industrial rooftops, where the ideal high module-to-ground height for optimal bPV performances is not feasible due to structural reasons. The experimental setup involved measuring the I-V curves of conventional and bifacial modules under diverse atmospheric conditions, including different solar irradiance levels and ambient temperatures, as well as mounting configurations. The results show a proportional increment of power generation between 4.3% and 7.8% if compared with two different conventional modules and a bifacial power gain between 2 and 15% under identical conditions. Additionally, the negative potential influence of the mounting structure was observed. Small differences in the alignment between the module and structural beams can virtually eliminate the bifacial contribution, with an estimated reduction up to 8.5 W (a potential bifacial gain of 3.43%). Full article
(This article belongs to the Special Issue Photovoltaic Power System: Modeling and Performance Analysis, 2nd Edition)
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22 pages, 5466 KiB  
Article
A Hybrid Convolutional–Long Short-Term Memory–Attention Framework for Short-Term Photovoltaic Power Forecasting, Incorporating Data from Neighboring Stations
by Feng Hu, Linghua Zhang and Jiaqi Wang
Appl. Sci. 2024, 14(12), 5189; https://doi.org/10.3390/app14125189 - 14 Jun 2024
Viewed by 653
Abstract
To enhance the safety of grid operations, this paper proposes a high-precision short-term photovoltaic (PV) power forecasting method that integrates information from surrounding PV stations and deep learning prediction models. The proposed method utilizes numerical weather prediction (NWP) data of the target PV [...] Read more.
To enhance the safety of grid operations, this paper proposes a high-precision short-term photovoltaic (PV) power forecasting method that integrates information from surrounding PV stations and deep learning prediction models. The proposed method utilizes numerical weather prediction (NWP) data of the target PV station and highly correlated features from nearby stations as inputs. This study first analyzes the correlation between irradiance and power sequences and calculates a comprehensive similarity index based on distance factors. Stations with high-similarity indices are selected as data sources. Subsequently, Bayesian optimization is employed to determine the optimal data fusion ratio. The selected data are then used to model power predictions through the convolutional long short-term memory with attention (Conv-LSTM-ATT) deep neural network. Experimental results show that the proposed model significantly outperforms three classical models in terms of forecasting accuracy. The data fusion strategy determined by Bayesian optimization reduces the root mean square error (RMSE) of the test set by 20.04%, 28.24%, and 30.94% under sunny, cloudy, and rainy conditions, respectively. Full article
(This article belongs to the Special Issue Photovoltaic Power System: Modeling and Performance Analysis, 2nd Edition)
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Review

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31 pages, 2424 KiB  
Review
A Comprehensive Review of Sizing and Energy Management Strategies for Optimal Planning of Microgrids with PV and Other Renewable Integration
by Fadi Agha Kassab, Rusber Rodriguez, Berk Celik, Fabrice Locment and Manuela Sechilariu
Appl. Sci. 2024, 14(22), 10479; https://doi.org/10.3390/app142210479 - 14 Nov 2024
Viewed by 627
Abstract
This article comprehensively reviews strategies for optimal microgrid planning, focusing on integrating renewable energy sources. The study explores heuristic, mathematical, and hybrid methods for microgrid sizing and optimization-based energy management approaches, addressing the need for detailed energy planning and seamless integration between these [...] Read more.
This article comprehensively reviews strategies for optimal microgrid planning, focusing on integrating renewable energy sources. The study explores heuristic, mathematical, and hybrid methods for microgrid sizing and optimization-based energy management approaches, addressing the need for detailed energy planning and seamless integration between these stages. Key findings emphasize the importance of optimal sizing to minimize costs and reduce carbon dioxide (CO2) emissions while ensuring system reliability. In a pedagogical manner, this review highlights the integrated methodologies that simultaneously address sizing and energy management and the potential of emerging technologies, such as smart grids and electric vehicles, to enhance energy efficiency and sustainability. This study outlines the importance of accurate load modeling and carefully selecting models for renewable energy sources and energy storage systems, including degradation models, to achieve long-term operational efficiency and sustainability in microgrid design and operation. Future research should focus on developing multi-objective optimization techniques and incorporating cutting-edge technologies for improved microgrid planning and operation. Full article
(This article belongs to the Special Issue Photovoltaic Power System: Modeling and Performance Analysis, 2nd Edition)
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