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Hybrid-Renewable Energy Systems in Microgrids
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Hybrid-Renewable Energy Systems in Microgrids

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 6546

Special Issue Editor

Dr. Sarvar Hussain Nengroo

E-Mail Website
Guest Editor
Cho Chun Shik Graduate School of Mobility, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea
Interests: smart grid; renewable energy; electronics; batteries; hybrid electric vehicles; lithium-ion batteries

Special Issue Information

Dear Colleagues,

During the last decade, microgrids have been developed as a tool to conceive different kinds of energy generation, storage, and consumption on a local scale. A microgrid is an autonomous entity that can operate in connection with the main utility grid or can become disconnected in islanded mode. These small-scale grids can also be connected, forming energy clusters. This Special Issue of Energies will explore the latest developments in technology to enable the widespread diffusion of microgrids throughout the globe. While papers concerning the control of microgrids systems are welcomed, we would particularly welcome those that offer insights into microgrid architectures and sites. The Special Issue will include, but is not limited to, the following:

  • Decentralized, distributed, and centralized controllers for microgrids;
  • Power quality for grid-connected and islanded microgrids;
  • Communication systems oriented to microgrids;
  • Energy management systems for microgrids;
  • Demonstration and pilot projects.

We welcome papers on primary, blue-skies research, as well as cutting-edge exemplars from industrial practice that can be used to encourage sustainable development and performance of energy microgrids worldwide.

Dr. Sarvar Hussain Nengroo
Guest Editor

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. 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

  • microgrids
  • distributed generation
  • islanded systems
  • renewable energy

Published Papers (6 papers)

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Research

13 pages, 1315 KiB  
Article
Reconceptualizing Reliability Indices as Metrics to Quantify Power Distribution System Resilience
by Gerald A. Abantao, Jessa A. Ibañez, Paul Eugene Delfin C. Bundoc, Lean Lorenzo F. Blas, Xaviery N. Penisa, Eugene A. Esparcia, Jr., Michael T. Castro, Roger Victor E. Buendia, Karl Ezra S. Pilario, Adonis Emmanuel D. Tio, Ivan Benedict Nilo C. Cruz, Joey D. Ocon and Carl Michael F. Odulio
Energies 2024, 17(8), 1909; https://doi.org/10.3390/en17081909 - 17 Apr 2024
Viewed by 529
Abstract
In regions heavily affected by recurrent typhoons, the need for more resilient electricity infrastructure is pressing. This emphasizes the importance of integrating resilience assessment, including incorporating resilience metrics, into the planning process of power distribution systems against any disruptive events. Although standardized metrics [...] Read more.
In regions heavily affected by recurrent typhoons, the need for more resilient electricity infrastructure is pressing. This emphasizes the importance of integrating resilience assessment, including incorporating resilience metrics, into the planning process of power distribution systems against any disruptive events. Although standardized metrics exist for assessing distribution system reliability, the absence of formalized resilience metrics hampers informed investments in critical infrastructure such as microgrid development. In this work, a set of resilience metrics is proposed by reconceptualizing reliability metrics. The metrics were formulated to account for both the type of extreme event and its specific impact on loads with varying levels of criticality. The effectiveness of the proposed metrics is demonstrated through a Philippine microgrid case study. A Monte Carlo framework incorporating an extreme event model, component fragility model, and system response model was used to quantify the resilience improvement before and after stand-alone microgrid operation of the power distribution system. Results show that the proposed metrics can effectively evaluate resilience enhancement and highlight the value of a holistic approach of considering critical loads and types of extreme events to strengthen societal and community resilience, making a compelling case for strategic investments in infrastructure upgrades such as microgrids. Full article
(This article belongs to the Special Issue Hybrid-Renewable Energy Systems in Microgrids)
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21 pages, 12433 KiB  
Article
Comparative Analysis Using Multiple Regression Models for Forecasting Photovoltaic Power Generation
by Burhan U Din Abdullah, Shahbaz Ahmad Khanday, Nair Ul Islam, Suman Lata, Hoor Fatima and Sarvar Hussain Nengroo
Energies 2024, 17(7), 1564; https://doi.org/10.3390/en17071564 - 25 Mar 2024
Viewed by 592
Abstract
Effective machine learning regression models are useful toolsets for managing and planning energy in PV grid-connected systems. Machine learning regression models, however, have been crucial in the analysis, forecasting, and prediction of numerous parameters that support the efficient management of the production and [...] Read more.
Effective machine learning regression models are useful toolsets for managing and planning energy in PV grid-connected systems. Machine learning regression models, however, have been crucial in the analysis, forecasting, and prediction of numerous parameters that support the efficient management of the production and distribution of green energy. This article proposes multiple regression models for power prediction using the Sharda University PV dataset (2022 Edition). The proposed regression model is inspired by a unique data pre-processing technique for forecasting PV power generation. Performance metrics, namely mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), R2-score, and predicted vs. actual value plots, have been used to compare the performance of the different regression. Simulation results show that the multilayer perceptron regressor outperforms the other algorithms, with an RMSE of 17.870 and an R2 score of 0.9377. Feature importance analysis has been performed to determine the most significant features that influence PV power generation. Full article
(This article belongs to the Special Issue Hybrid-Renewable Energy Systems in Microgrids)
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28 pages, 4995 KiB  
Article
The Development of a Reduced-Scale Laboratory for the Study of Solutions for Microgrids
by Bruno Pinto Braga Guimaraes, Ronny Francis Ribeiro Junior, Marcos Vinicius Andrade, Isac Antonio dos Santos Areias, Joao Gabriel Luppi Foster, Erik Leandro Bonaldi, Frederico de Oliveira Assuncao, Levy Ely de Lacerda de Oliveira, Fabio Monteiro Steiner and Yasmina El-Heri
Energies 2024, 17(3), 609; https://doi.org/10.3390/en17030609 - 26 Jan 2024
Viewed by 497
Abstract
The integration of renewable energy sources is crucial for achieving sustainability and environmental preservation. However, their intermittent nature poses challenges to electrical system stability, requiring robust integration strategies. Microgrids emerge as a flexible solution, but their successful deployment requires meticulous planning and intelligent [...] Read more.
The integration of renewable energy sources is crucial for achieving sustainability and environmental preservation. However, their intermittent nature poses challenges to electrical system stability, requiring robust integration strategies. Microgrids emerge as a flexible solution, but their successful deployment requires meticulous planning and intelligent operation to overcome these challenges. This paper presents the development of a reduced-scale laboratory dedicated to researching both hardware and software solutions for intelligent microgrid management. The laboratory was designed to incorporate key components that are becoming increasingly important in the present microgrid context, including renewable energy generation, storage systems, electrolyzers for hydrogen production, and combined heat and power sources. While some equipment consists of commercial models, the battery bank, converter, and supervisory systems were custom-designed to meet the specific requirements of the laboratory. The laboratory has proven itself as a robust tool for conducting studies on microgrids, effectively incorporating essential components, addressing pertinent system issues, and allowing for several tests on converting control algorithms. Full article
(This article belongs to the Special Issue Hybrid-Renewable Energy Systems in Microgrids)
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20 pages, 5088 KiB  
Article
Day-Ahead Operational Planning for DisCos Based on Demand Response Flexibility and Volt/Var Control
by Mauro Jurado, Eduardo Salazar, Mauricio Samper, Rodolfo Rosés and Diego Ojeda Esteybar
Energies 2023, 16(20), 7045; https://doi.org/10.3390/en16207045 - 11 Oct 2023
Viewed by 876
Abstract
Considering the integration of distributed energy resources (DER) such as distributed generation, demand response, and electric vehicles, day-ahead scheduling plays a significant role in the operation of active distribution systems. Therefore, this article proposes a comprehensive methodology for the short-term operational planning of [...] Read more.
Considering the integration of distributed energy resources (DER) such as distributed generation, demand response, and electric vehicles, day-ahead scheduling plays a significant role in the operation of active distribution systems. Therefore, this article proposes a comprehensive methodology for the short-term operational planning of a distribution company (DisCo), aiming to minimize the total daily operational cost. The proposed methodology integrates on-load tap changers, capacitor banks, and flexible loads participating in demand response (DR) to reduce losses and manage congestion and voltage violations, while considering the costs associated with the operation and use of controllable resources. Furthermore, to forecast PV output and load demand behind the meter at the MV/LV distribution transformer level, a short-term net load forecasting model using deep learning techniques has been incorporated. The proposed scheme is solved through an efficient two-stage strategy based on genetic algorithms and dynamic programming. Numerical results based on the modified IEEE 13-node distribution system and a typical 37-node Latin American system validate the effectiveness of the proposed methodology. The obtained results verify that, through the proposed methodology, the DisCo can effectively schedule its installations and DR to minimize the total operational cost while reducing losses and robustly managing voltage and congestion issues. Full article
(This article belongs to the Special Issue Hybrid-Renewable Energy Systems in Microgrids)
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21 pages, 3795 KiB  
Article
An Improved Artificial Ecosystem-Based Optimization Algorithm for Optimal Design of a Hybrid Photovoltaic/Fuel Cell Energy System to Supply A Residential Complex Demand: A Case Study for Kuala Lumpur
by Jing Yang, Yen-Lin Chen, Por Lip Yee, Chin Soon Ku and Manoochehr Babanezhad
Energies 2023, 16(6), 2867; https://doi.org/10.3390/en16062867 - 20 Mar 2023
Cited by 3 | Viewed by 1365
Abstract
In this paper, the optimal design of a hybrid energy system (HES), consisting of photovoltaic technology integrated with fuel cells (HPV/FC) and relying on hydrogen storage, is performed to meet the annual demand of a residential complex to find the minimum total net [...] Read more.
In this paper, the optimal design of a hybrid energy system (HES), consisting of photovoltaic technology integrated with fuel cells (HPV/FC) and relying on hydrogen storage, is performed to meet the annual demand of a residential complex to find the minimum total net present cost (TNPC), while observing the reliability constraint as the energy-not-supplied probability (ENSP) and considering real meteorological data of the Kuala Lumpur city in Malaysia. The decision variables include the size of system components, which are optimally determined by an improved artificial ecosystem-based optimization algorithm (IAEO). The conventional AEO is improved using the dynamic lens-imaging learning strategy (DLILS) to prevent premature convergence. The results demonstrated that the decrease (increase) of the reliability constraint leads to an increase (decrease) in the TNPC, as well as the cost of electricity (COE). For a maximum reliability constraint of 5%, the results show that the TNPC and COE obtained USD 2.247 million and USD 0.4046 million, respectively. The superior performance of the IAEO has been confirmed with the AEO, particle swarm optimization (PSO), and manta ray foraging optimization (MRFO), with the lowest TNPC and higher reliability. In addition, the effectiveness of the hydrogen tank efficiency and load changes is confirmed in the hybrid system design. Full article
(This article belongs to the Special Issue Hybrid-Renewable Energy Systems in Microgrids)
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19 pages, 4230 KiB  
Article
Multi-Objective Hybrid Optimization for Optimal Sizing of a Hybrid Renewable Power System for Home Applications
by Md. Arif Hossain, Ashik Ahmed, Shafiqur Rahman Tito, Razzaqul Ahshan, Taiyeb Hasan Sakib and Sarvar Hussain Nengroo
Energies 2023, 16(1), 96; https://doi.org/10.3390/en16010096 - 21 Dec 2022
Cited by 5 | Viewed by 1961
Abstract
An optimal energy mix of various renewable energy sources and storage devices is critical for a profitable and reliable hybrid microgrid system. This work proposes a hybrid optimization method to assess the optimal energy mix of wind, photovoltaic, and battery for a hybrid [...] Read more.
An optimal energy mix of various renewable energy sources and storage devices is critical for a profitable and reliable hybrid microgrid system. This work proposes a hybrid optimization method to assess the optimal energy mix of wind, photovoltaic, and battery for a hybrid system development. This study considers the hybridization of a Non-dominant Sorting Genetic Algorithm II (NSGA II) and the Grey Wolf Optimizer (GWO). The objective function was formulated to simultaneously minimize the total energy cost and loss of power supply probability. A comparative study among the proposed hybrid optimization method, Non-dominant Sorting Genetic Algorithm II, and multi-objective Particle Swarm Optimization (PSO) was performed to examine the efficiency of the proposed optimization method. The analysis shows that the applied hybrid optimization method performs better than other multi-objective optimization algorithms alone in terms of convergence speed, reaching global minima, lower mean (for minimization objective), and a higher standard deviation. The analysis also reveals that by relaxing the loss of power supply probability from 0% to 4.7%, an additional cost reduction of approximately 12.12% can be achieved. The proposed method can provide improved flexibility to the stakeholders to select the optimum combination of generation mix from the offered solutions. Full article
(This article belongs to the Special Issue Hybrid-Renewable Energy Systems in Microgrids)
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