Advanced Technologies for Renewable Energy Systems and Their Applications

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

Deadline for manuscript submissions: 15 October 2024 | Viewed by 4427

Special Issue Editors


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Guest Editor
1. Department of Engineering, University of Trás-os-Montes and Alto Douro, 5001-801 Vila Real, Portugal
2. CPES INESCTEC-Center for Power and Energy System, University of Trás-os-Montes e Alto Douro, 5001-801 Vila Real, Portugal
Interests: power system quality; harmonic distortion; monitorization systems; renewables; microgeneration; electrical machines
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
1. Department of Engineering, University of Trás-os-Montes e Alto Douro, 5001-801 Vila Real, Portugal
2. INESCTEC, University of Trás-os-Montes e Alto Douro, 5001-801 Vila Real, Portugal
Interests: artificial intelligence; machine learning; multi-agent systems; electricity markets; smart grid
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The implementation of initiatives and investments has caused a significant increase in renewable energy sources usage. However, these energy systems cannot efficiently cope with changes. The variable nature of most renewable generation sources presents challenging stochasticity problems, which must be solved to enable a smooth energy transition. Moreover, new energy resources, players and business models are emerging, related, e.g., to the quick widespread of electric vehicles, the integration of energy storage systems or the emergence of novel consumer-focused initiatives and electricity market models. Consequently, advanced and innovative technologies are urgently required to support new and essential planning, management and operation models.

This Special Issue calls for contributions related to renewable energy systems, addressing innovative perspectives and related problems. These include, but are not limited to, works that concern novel conceptual energy planning, management and operation models; new software solutions for renewable energy or related resources’ integration; practical applications, case studies and demonstrations of advanced technological solutions.

 Topics:

  • Case studies and practical demonstrations;
  • Electric vehicles integration;
  • Energy resources management;
  • Energy systems forecasting;
  • Local electricity market models;
  • Network optimization models;
  • Power quality;
  • Power systems management;
  • Renewable energy;
  • Renewable energy applications;
  • Smart grids;
  • Storage systems integration;
  • Predictions methods for renewable energy forecasting;
  • Optimization applied to renewable systems management;
  • EV Charging systems with the incorporation of renewables;
  • Energy sharing models in renewable energy communities;
  • Advances in photovoltaic technologies.

Prof. Dr. José Manuel Ribeiro Baptista
Prof. Dr. Tiago Pinto
Guest Editors

Manuscript Submission Information

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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. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

  • renewable energy
  • electric vehicles
  • energy storage systems
  • energy system applications
  • power system management
  • energy forecasting

Published Papers (4 papers)

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Research

18 pages, 8503 KiB  
Article
Imitation Learning-Based Energy Management Algorithm: Lille Catholic University Smart Grid Demonstrator Case Study
by Taheni Swibki, Ines Ben Salem, Youssef Kraiem, Dhaker Abbes and Lilia El Amraoui
Electronics 2023, 12(24), 5048; https://doi.org/10.3390/electronics12245048 - 18 Dec 2023
Cited by 1 | Viewed by 857
Abstract
This paper proposes a novel energy management approach (imitation-Q-learning) based on imitation learning (IL) and reinforcement learning (RL). The proposed approach reinforces a decision-making agent based on a modified Q-learning algorithm to mimic an expert demonstration to solve a microgrid (MG) energy management [...] Read more.
This paper proposes a novel energy management approach (imitation-Q-learning) based on imitation learning (IL) and reinforcement learning (RL). The proposed approach reinforces a decision-making agent based on a modified Q-learning algorithm to mimic an expert demonstration to solve a microgrid (MG) energy management problem. Those demonstrations are derived from solving a set of linear programming (LP) problems. Consequently, the imitation-Q-learning algorithm learns by interacting with the MG simulator and imitating the LP demonstrations to make decisions in real time that minimize the MG energy costs without prior knowledge of uncertainties related to photovoltaic (PV) production, load consumption, and electricity prices. A real-scale MG at the Lille Catholic University in France was used as a case study to conduct experiments. The proposed approach was compared to the expert performances, which are the LP algorithm and the conventional Q-learning algorithm in different test scenarios. It was approximately 80 times faster than conventional Q-learning and achieved the same performance as LP. In order to test the robustness of the proposed approach, a PV inverter crush and load shedding were also simulated. Preliminary results show the effectiveness of the proposed method. Full article
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15 pages, 1522 KiB  
Article
Research on Orderly Charging Strategy for Electric Vehicles Based on Electricity Price Guidance and Reliability Evaluation of Microgrid
by Zhipeng Weng, Jinghua Zhou, Xiaotong Song and Liuming Jing
Electronics 2023, 12(23), 4876; https://doi.org/10.3390/electronics12234876 - 4 Dec 2023
Viewed by 958
Abstract
With the increasing use of electric vehicles (EVs), EVs will be widely connected to the microgrid in the future. However, the influence of the disorderly charging behavior of EVs on the stable and reliable operation of the power grid cannot be ignored. To [...] Read more.
With the increasing use of electric vehicles (EVs), EVs will be widely connected to the microgrid in the future. However, the influence of the disorderly charging behavior of EVs on the stable and reliable operation of the power grid cannot be ignored. To address these challenges, the charging load characteristic model is established to describe the charging behavior of EVs. Then, an EVs orderly charging strategy based on electricity price guidance is proposed, and the goal is to minimize the peak–valley difference ratio and the total cost of EV charging. The result shows that, compared with disorderly charging, the EV orderly charging strategy based on electricity price guidance proposed in this paper can effectively reduce the peaking and valley difference ratio of load, reduce user’s charging costs, and optimize the reliability level of the microgrid. Full article
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14 pages, 2989 KiB  
Article
Remaining Useful Life Prediction Method of PEM Fuel Cells Based on a Hybrid Model
by Qiancheng Tian, Haitao Chen, Shuai Ding, Lei Shu, Lei Wang and Jun Huang
Electronics 2023, 12(18), 3883; https://doi.org/10.3390/electronics12183883 - 14 Sep 2023
Cited by 1 | Viewed by 1122
Abstract
To predict the remaining useful life (RUL) of the proton exchange membrane fuel cell (PEMFC) in advance, a prediction method based on the voltage recovery model and Bayesian optimization of a multi-kernel relevance vector machine (MK-RVM) is proposed in this paper. First, the [...] Read more.
To predict the remaining useful life (RUL) of the proton exchange membrane fuel cell (PEMFC) in advance, a prediction method based on the voltage recovery model and Bayesian optimization of a multi-kernel relevance vector machine (MK-RVM) is proposed in this paper. First, the empirical mode decomposition (EMD) method was used to preprocess the data, and then MK-RVM was used to train the model. Next, the Bayesian optimization algorithm was used to optimize the weight coefficient of the kernel function to complete the parameter update of the prediction model, and the voltage recovery model was added to the prediction model to realize the rapid and accurate prediction of the RUL of PEMFC. Finally, the method proposed in this paper was applied to the open data set of PEMFC provided by Fuel Cell Laboratory (FCLAB), and the prediction accuracy of RUL for PEMFC was obtained by 95.35%, indicating that this method had good generalization ability and verified the accuracy of the method when predicting the RUL of PEMFC. The realization of long-term projections for PEMFC RUL not only improves the useful life, reliability, and safety of PEMFC but also reduces operating costs and downtime. Full article
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19 pages, 6876 KiB  
Article
Assessment of Energy Conversion in Passive Components of Single-Phase Photovoltaic Systems Interconnected to the Grid
by Heriberto Adamas-Pérez, Mario Ponce-Silva, Jesús Darío Mina-Antonio, Abraham Claudio-Sánchez and Omar Rodríguez-Benítez
Electronics 2023, 12(15), 3341; https://doi.org/10.3390/electronics12153341 - 4 Aug 2023
Cited by 1 | Viewed by 823
Abstract
This paper presents a mathematical analysis of how energy return in grid-connected single-phase photovoltaic systems affects the sizing of passive components. Energy return affects the size of the link capacitor, making it larger than reported in the literature. One of the main points [...] Read more.
This paper presents a mathematical analysis of how energy return in grid-connected single-phase photovoltaic systems affects the sizing of passive components. Energy return affects the size of the link capacitor, making it larger than reported in the literature. One of the main points of this article is that an inverter connected to the grid using a DC–DC converter with an appropriate link capacitor is analyzed. The energy return is caused by the value (in Henry units) of the L-filter, which is also analyzed in this paper. The analysis shows that there is a link between the value of the L-filter and the voltage of the DC bus. The analysis assumes two conditions: (1) the DC bus voltage is always higher than the peak value of the grid sinusoidal voltage, and (2) there is a unity power factor at the connection point between the grid and the L-filter. To operate in an open loop, a compensation phase angle is calculated and introduced in the single-phase inverter modulation; this phase angle compensates the phase shift caused by the L-filter, avoiding the use of a phase-locked-loop (PLL) control system. The L-filter ripple current is evaluated by Fourier analysis, and the DC bus ripple voltage is evaluated by considering the energy returned to the link capacitor. The results of the analyses are compared with existing methods reported in the literature. The results also show that, to minimize the value of the L-filter, the DC voltage must be almost equal to the maximum voltage of the grid. Equations to assess the value of the DC-link capacitor and the L-filter in function of their ripples are developed. The results were verified with simulations in Simulink and experimentally. Full article
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