Trends in Photovoltaic Systems for Enhanced Power Generation and Energy Efficiency

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Power Electronics".

Deadline for manuscript submissions: 15 June 2024 | Viewed by 3605

Special Issue Editors


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Guest Editor
Department of Electronic and Automatic Engineering, University of Jaen, 23071 Jaen, Spain
Interests: solar radiation; energy harvesting; photovoltaic systems; monitoring; performance analysis of photovoltaic generator; self-consumption; microgrids
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Electronic and Automatic Engineering, University of Jaén, Campus Lagunillas, 23071 Jaén, Spain
Interests: photovoltaic self-consumption systems; photovoltaic systems monitoring; performance analysis of photovoltaic systems from monitored data; microgrids
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Electrical Engineering, Campus Lagunillas, University of Jaen, 23071 Jaen, Spain
Interests: photovoltaic systems; monitoring; performance analysis; self-consumption; microgrids; smart home; smart building; smart environment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Energy systems should meet the objectives of sustainable development, which are based on three pillars: economic development, environmental management and social equity. Therefore, fossil fuels should be gradually substituted for renewable energy sources and promote self-consumption where photovoltaics may take a prominent role. However, electricity generated from this type of source does not always provide an immediate response to demand due to the fluctuating nature of photovoltaics. Still, many challenges may be faced from a technical perspective in order to develop smart grid technology through intelligent load management, power electronic converters for interconnection to the grid, power quality, the incorporation of electric vehicles, as well as the use of energy storage systems, which may mitigate the volatility in power generation with renewable energies and ensure the stability of electricity grids. In this sense, smart microgrids may be key for improving the integration of renewable energies in electrical energy systems.

This Special Issue aims to explore different issues related to the grid integration of photovoltaic systems. Moreover, it will analyze the advances in smart microgrids, incorporating new technologies that can improve the power quality (such as voltage stability, frequency stability, and harmonic distortion) of such grids. This Special Issue focuses on, but is not limited to, the following topics:

  • Planning and operation of photovoltaic power systems;
  • Monitoring, control, and management of PV systems;
  • Modeling of smart grids with PV systems;
  • Energy management and intelligent control for photovoltaic rooftops in the residential and industrial sector;
  • Economic analysis of PV systems;
  • Power quality enhancement of PV systems;
  • Energy performance;
  • Energy storage.

Prof. Dr. Catalina Rus-Casas
Prof. Dr. Francisco José Muñoz-Rodríguez
Dr. Gabino Jiménez Castillo
Guest Editors

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Keywords

  • grid reliability and stability
  • control of power electronic converter system
  • monitoring and measurement
  • approaches for the economic analysis of grid integration
  • photovoltaic rooftops
  • electric vehicle
  • energy storage
  • distributed generation
  • microgrid
  • smart grid

Published Papers (3 papers)

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Research

30 pages, 4665 KiB  
Article
Two-Stage Neural Network Optimization for Robust Solar Photovoltaic Forecasting
by Jinyeong Oh, Dayeong So, Jaehyeok Jo, Namil Kang, Eenjun Hwang and Jihoon Moon
Electronics 2024, 13(9), 1659; https://doi.org/10.3390/electronics13091659 - 25 Apr 2024
Viewed by 478
Abstract
Neural networks (NNs) have shown outstanding performance in solar photovoltaic (PV) power forecasting due to their ability to effectively learn unstable environmental variables and their complex interactions. However, NNs are limited in their practical industrial application in the energy sector because the optimization [...] Read more.
Neural networks (NNs) have shown outstanding performance in solar photovoltaic (PV) power forecasting due to their ability to effectively learn unstable environmental variables and their complex interactions. However, NNs are limited in their practical industrial application in the energy sector because the optimization of the model structure or hyperparameters is a complex and time-consuming task. This paper proposes a two-stage NN optimization method for robust solar PV power forecasting. First, the solar PV power dataset is divided into training and test sets. In the training set, several NN models with different numbers of hidden layers are constructed, and Optuna is applied to select the optimal hyperparameter values for each model. Next, the optimized NN models for each layer are used to generate estimation and prediction values with fivefold cross-validation on the training and test sets, respectively. Finally, a random forest is used to learn the estimation values, and the prediction values from the test set are used as input to predict the final solar PV power. As a result of experiments in the Incheon area, the proposed method is not only easy to model but also outperforms several forecasting models. As a case in point, with the New-Incheon Sonae dataset—one of three from various Incheon locations—the proposed method achieved an average mean absolute error (MAE) of 149.53 kW and root mean squared error (RMSE) of 202.00 kW. These figures significantly outperform the benchmarks of attention mechanism-based deep learning models, with average scores of 169.87 kW for MAE and 232.55 kW for RMSE, signaling an advance that is expected to make a significant contribution to South Korea’s energy industry. Full article
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27 pages, 5459 KiB  
Article
Robust Nonsingular Terminal Sliding Mode Control of a Buck Converter Feeding a Constant Power Load
by Khalil Louassaa, Aissa Chouder and Catalina Rus-Casas
Electronics 2023, 12(3), 728; https://doi.org/10.3390/electronics12030728 - 1 Feb 2023
Cited by 2 | Viewed by 1616
Abstract
In recent years, DC microgrid systems feeding constant power loads (CPLs) have been given a particular focus due to their effect on the overall system stability caused by their electrical characteristics behaving as negative incremental impedance. To address this issue, this paper investigates [...] Read more.
In recent years, DC microgrid systems feeding constant power loads (CPLs) have been given a particular focus due to their effect on the overall system stability caused by their electrical characteristics behaving as negative incremental impedance. To address this issue, this paper investigates the stabilization of a DC bus voltage in a DC microgrid (MG) feeding a CPL. The output voltage of the main DC bus is stabilized by using a robust nonsingular terminal sliding mode controller that is characterized by the elimination of the singularity problem that arises from the conventional terminal sliding mode controller. The CPL is emulated by a boost converter where its output voltage is tightly regulated. The system is investigated in terms of voltage following and disturbance rejection. The robustness and effectiveness of the proposed control strategy are assessed against input voltage fluctuations and power demand variations. The proposed controller is validated through simulations and an experimental setup. Full article
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32 pages, 1612 KiB  
Article
Photovoltaic System for Microinverter Applications Based on a Non-Electrolytic-Capacitor Boost Converter and a Sliding-Mode Controller
by Carlos Andres Ramos-Paja, Oscar Danilo-Montoya and Luis Fernando Grisales-Noreña
Electronics 2022, 11(18), 2923; https://doi.org/10.3390/electronics11182923 - 15 Sep 2022
Cited by 5 | Viewed by 1211
Abstract
This paper presents a photovoltaic (PV) system designed to reduce the DC-link capacitance present in double-stage PV microinverters without increasing the capacitor interfacing the PV source. This solution is based on a modified boost topology, which exhibits continuous current in both input and [...] Read more.
This paper presents a photovoltaic (PV) system designed to reduce the DC-link capacitance present in double-stage PV microinverters without increasing the capacitor interfacing the PV source. This solution is based on a modified boost topology, which exhibits continuous current in both input and output ports. Such a characteristic enables the implementation of PV microinverters without electrolytic capacitors, which improves the reliability in comparison with solutions based on classical converters with discontinuous output current and electrolytic capacitors. However, the modified boost converter exhibits different dynamic behavior in comparison with the classical boost converter; thus, design processes and controllers developed for the classical boost converter are not applicable. This paper also introduces a sliding-mode controller designed to ensure the stable operation of the PV microinverter around the maximum power point. Moreover, this solution also rejects the voltage oscillations at double the grid frequency generated by the grid-connection. The global stability of the complete PV system is formally demonstrated using mathematical analyses, and a step-by-step design process for both the power stage and control system is proposed. Finally, the design process is illustrated using a representative application example, and the correct operation of the PV system is validated using realistic circuital simulations. The results validate the accuracy of the theoretical equations proposed for both the design and control of the novel PV system, where errors below 4.5% were obtained for the ripple prediction, and below 1% for the prediction of the dynamic behavior. Full article
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