Submit to Special Issue Submit Abstract to Special Issue Review for Energies Propose a Special Issue

Journal Menu

Journal Browser

New Trends in Photovoltaic Power System

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A2: Solar Energy and Photovoltaic Systems".

Deadline for manuscript submissions: 30 May 2026 | Viewed by 6298

Share This Special Issue

Special Issue Editors

Prof. Dr. Santiago Silvestre

E-Mail Website
Guest Editor

Department of Electronic Engineering, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain
Interests: photovoltaic modules; photovoltaic systems; fault detection; modeling and simulation of PV systems
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Aissa Chouder

E-Mail Website
Guest Editor

Department of Electrical Engineering, Electrical Engineering Laboratory, University of M'sila, M'sila 28000, Algeria
Interests: photovoltaic modules; photovoltaic system; microgrids; current control; renewable energy sources; simulation results

Special Issue Information

Dear Colleagues,

Photovoltaic (PV) systems have seen significant growth in global energy production over the past decades. Actual projections suggest it could exceed 10% of global electricity generation by 2030. Moreover, the PV system industry is evolving rapidly with new technologies, strategies, and innovations aimed at improving efficiency, reducing costs, and making solar energy more sustainable. Some key trends shaping the future of PV systems include the following:

-Bifacial solar panels, perovskite solar cells, and thin-film materials are emerging technologies that can help improve energy yield from the same surface area, making PV systems more efficient and cost-effective and allowing for integration into more diverse applications.
-PV modules with transparent and flexible design are also an interesting emerging trend. These PV modules are integrated into windows, facades, and other surfaces without disrupting the esthetics or function of buildings. Their application in building-integrated photovoltaics (BIPV) is an interesting option in urban environments where rooftop space may be limited.
-Solar tracking systems can improve the energy output of PV systems by adjusting the angle of PV modules to follow the sun’s path, maximizing exposure and efficiency and making them viable for large-scale solar farms.
-In the field of energy storage integration and smart grids, energy storage systems (ESSs) allow excess PV energy to be stored for use when sunlight is not available. The EES solutions increase the self-consumption, reduce reliance on the grid, and support the transition to renewable energy in areas with unstable or inadequate electricity infrastructure.
-PV and artificial intelligence (AI) can be used for the predictive maintenance and optimization of the performance of PV systems. This includes predictive algorithms that can detect issues before they cause significant downtime and real-time performance optimization including degradation monitoring.
-Hybrid solar systems combine PV with other forms of renewable energy, like wind, biomass, or with traditional power sources such as natural gas generators to improve reliability and energy production.
-Agri-Voltaics integrates PV generation with agricultural practices, allowing farmers to grow crops under or between PV modules. The modules provide shade and reduce water evaporation, while still generating power, helping to meet global food and energy demands in a sustainable way.
-Floating photovoltaics deals with PV modules that are mounted on floating platforms and promise high yields. Their proximity to water could support the cooling of solar cells, thus enabling them to work efficiently even in hot weather conditions.

As these trends continue to evolve, the PV sector will play a central role in meeting the world’s growing energy demands in a sustainable manner. This Special Issue aims to collect original research or review articles on new trends in PV power systems from an applied point of view.

Prof. Dr. Santiago Silvestre
Prof. Dr. Aissa Chouder
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 250 words) can be sent to the Editorial Office for assessment.

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. Energies 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 2600 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

emerging PV technologies
artificial intelligence (AI)
energy storage systems (ESS)
smart grids
agri-photovoltaics
floating PV

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

37 pages, 20396 KB

Open AccessArticle

Comparative Analysis of Peer-to-Peer Energy Trading with Multi-Objective Optimization in Rooftop Photovoltaics-Powered Residential Community

by Mohammad Zeyad, Berk Celik, Timothy M. Hansen, Fabrice Locment and Manuela Sechilariu

Energies 2026, 19(5), 1231; https://doi.org/10.3390/en19051231 - 1 Mar 2026

Cited by 1 | Viewed by 815

Abstract

The rapid growth of distributed solar energy, such as rooftop photovoltaics (PVs), has revolutionized conventional power systems into more distributed networks, enabling end-users to engage in and trade within the energy market. Maximizing the benefits of rooftop PV panels for residential end-users, including increased renewable energy use and reduced reliance on the utility grid, remains an essential challenge in conventional centralized markets. Moreover, reducing energy consumption may lead to increased peak demand, decreased self-consumption, reduced system flexibility, and reduced grid stability. Therefore, this study presents a transactive energy market framework that integrates home energy management systems (HEMSs) with multi-objective optimization and an aggregator-based, distributed peer-to-peer (P2P) trading strategy to increase rooftop PV utilization and reduce grid dependency within an intra-residential community. The HEMS is structured to integrate rooftop PV production, battery energy storage systems, and smart appliances to offer flexibility through demand response programs in balancing supply and demand by scheduling appliances during periods of rooftop PV production and lower grid prices. Multi-objective (i.e., minimizing energy consumption cost and peak load) optimization problems are solved using the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) by achieving a Pareto-optimal solution. To validate the reliability and optimality of the NSGA-II results, the same problem formulation is solved using a mixed-integer linear programming approach. Moreover, a Strategic Double Auction with Dynamic Pricing (SDA-DP) strategy is proposed to support P2P trading among consumers and prosumers and thereafter compared with a rule-based zero-intelligence strategy with market-matching rules to analyze the trading performance of the proposed SDA-DP. The results of this comparative analysis (for 10 households, year-long simulation with 15 min time resolution) demonstrate that compared to the baseline case, integrating NSGA-II optimization with SDA-DP trading significantly enhances rooftop PV utilization by 35.11%, reduces grid dependency by 34.04%, and reduces electricity consumption costs by 30.53%, with savings of €1.93 to €6.67 for a single day after participating in the proposed P2P market. Full article

(This article belongs to the Special Issue New Trends in Photovoltaic Power System)

► Show Figures

Figure 1

23 pages, 3836 KB

Open AccessArticle

Integration of PV Systems in Urban Environments: Complementary Metrics to Analyze Their Performance

by Carlos Gilabert-Torres, Leocadio Hontoria-García, Juan Ignacio Fernández-Carrasco, Adel Mellit and Catalina Rus-Casas

Energies 2025, 18(16), 4411; https://doi.org/10.3390/en18164411 - 19 Aug 2025

Cited by 1 | Viewed by 1173

Abstract

The decarbonization of the energy sector drives the implementation of building-integrated and building-applied photovoltaic (BIPV–BAPV) systems. However, these systems face space and design limitations in urban environments. This study proposes an innovative methodology for the design and sizing of urban photovoltaic systems, considering diverse distributions and introducing metrics that link performance to occupied area. The methodology was applied to a university building in southern Spain, comparing the performance of rooftop photovoltaic (RTPV) and facade-applied photovoltaic (FAPV) systems. FAPV showed a larger useful area, resulting in similar self-sufficiency indices (RTPV: 22%, FAPV: 21%) and a 5% higher total emission reduction compared to the RTPV system. The proposed metrics demonstrate that FAPV outperforms RTPV both in final yield (49 vs. 21 kWh/kWp·m²) and total emission reduction (3.1 vs. 1.3 kgCO2eq/kWp·m²) normalized by installed power and occupied area. These complementary metrics are crucial for evaluating and selecting optimal photovoltaic configurations with varying generation densities and efficiencies, driving urban decarbonization and the creation of Zero Energy Buildings (ZEBs). Full article

(This article belongs to the Special Issue New Trends in Photovoltaic Power System)

► Show Figures

Figure 1

30 pages, 5283 KB

Open AccessArticle

Faults Detection and Diagnosis of a Large-Scale PV System by Analyzing Power Losses and Electric Indicators Computed Using Random Forest and KNN-Based Prediction Models

by Yasmine Gaaloul, Olfa Bel Hadj Brahim Kechiche, Houcine Oudira, Aissa Chouder, Mahmoud Hamouda, Santiago Silvestre and Sofiane Kichou

Energies 2025, 18(10), 2482; https://doi.org/10.3390/en18102482 - 12 May 2025

Cited by 10 | Viewed by 2577

Abstract

Accurate and reliable fault detection in photovoltaic (PV) systems is essential for optimizing their performance and durability. This paper introduces a novel approach for fault detection and diagnosis in large-scale PV systems, utilizing power loss analysis and predictive models based on Random Forest (RF) and K-Nearest Neighbors (KNN) algorithms. The proposed methodology establishes a predictive baseline model of the system’s healthy behavior under normal operating conditions, enabling real-time detection of deviations between expected and actual performance. Faults such as string disconnections, module short-circuits, and shading effects have been identified using two key indicators: current error (Ec) and voltage error (Ev). By focusing on power losses as a fault indicator, this method provides high-accuracy fault detection without requiring extensive labeled data, a significant advantage for large-scale PV systems where data acquisition can be challenging. Additionally, a key contribution of this work is the identification and correction of faulty sensors, specifically pyranometer misalignment, which leads to inaccurate irradiation measurements and disrupts fault diagnosis. The approach ensures reliable input data for the predictive models, where RF achieved an R² of 0.99657 for current prediction and 0.99459 for power prediction, while KNN reached an R² of 0.99674 for voltage estimation, improving both the accuracy of fault detection and the system’s overall performance. The outlined approach was experimentally validated using real-world data from a 500 kWp grid-connected PV system in Ain El Melh, Algeria. The results demonstrate that this innovative method offers an efficient, scalable solution for real-time fault detection, enhancing the reliability of large PV systems while reducing maintenance costs. Full article

(This article belongs to the Special Issue New Trends in Photovoltaic Power System)

► Show Figures

Figure 1

17 pages, 5552 KB

Open AccessArticle

MPPT-Based Chaotic ABC Algorithm for a Photovoltaic Power System Under Partial Shading Conditions

by Chian-Song Chiu and Yu-Ting Chen

Energies 2025, 18(7), 1710; https://doi.org/10.3390/en18071710 - 28 Mar 2025

Viewed by 945

Abstract

This paper presents a novel maximum power point tracking (MPPT) method designed for a photovoltaic (PV) power system operating under partial shading conditions. Partial shading conditions induce multiple power peak characteristics into the power–voltage curve of the PV power system, such that conventional MPPT methods often lead local maximum power and result in suboptimal energy harvesting. To solve this problem, this paper proposes a chaotic artificial bee colony (CABC) algorithm hybridized with a chaotic searching behavior. The incorporation of the chaotic mapping enhances the exploration capability of bees (i.e., faster convergence time) and escapes local optima. To demonstrate its superior performance, the CABC algorithm is rigorously evaluated through simulations under two distinct partial shading scenarios, while making comparisons with the standard ABC algorithm and traditional MPPT methods. Therefore, the potential of this novel approach enhances MPPT accuracy, efficiency, and reliability in a partially shaded PV power system. Full article

(This article belongs to the Special Issue New Trends in Photovoltaic Power System)

► Show Figures

Journal Menu

Journal Browser

New Trends in Photovoltaic Power System

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (4 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI