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Hybrid-Renewable Energy Systems in Microgrids

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

Deadline for manuscript submissions: closed (17 April 2025) | Viewed by 16478

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Special Issue Editor

Dr. Sarvar Hussain Nengroo

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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

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Keywords

microgrids
distributed generation
islanded systems
renewable energy

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Published Papers (9 papers)

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Research

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25 pages, 5345 KiB

Open AccessArticle

Collaborative Game Theory Between Microgrid Operators and Distribution System Operator Considering Multi-Faceted Uncertainties

by Shuai Wang, Xiaojing Ma, Yaling Yan, Tusongjiang Kari and Wei Zhang

Energies 2025, 18(7), 1577; https://doi.org/10.3390/en18071577 - 21 Mar 2025

Viewed by 217

Abstract

In the vigorous development of the power system, to address the economic challenges of multi-microgrid systems, this paper proposes a Nash bargaining model for collaboration between microgrid operators (MGs) and a distribution system operator (DSO) under conditions of multiple uncertainties. Firstly, a model for energy transactions between multiple complementary microgrid systems and a distribution system is established. Secondly, the chance-constrained method and robust optimization method are applied to model the multiple uncertainties in renewable energy generation and electricity trading prices. Moreover, using Nash bargaining theory, a cooperative operation model between MGs and a DSO is established, which is then transformed into two subproblems: cost minimization in cooperation and revenue maximization from power trading. To protect the privacy of each participant, a distributed solution approach using the alternating direction method of multipliers (ADMM) is applied to solve these subproblems. Finally, the simulation results indicate that the benefit values of all entities have improved after cooperative operation through the proposed model. Specifically, the benefit value of MG 1 is CNY 919,974.3, MG 2 is CNY 1,420,363.2, MG 3 is CNY 790,288.3, and the DSO is CNY 26,257.2. These results demonstrate that the proposed model has favorable economic performance. Full article

(This article belongs to the Special Issue Hybrid-Renewable Energy Systems in Microgrids)

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13 pages, 1315 KiB

Open AccessArticle

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

Cited by 1 | Viewed by 2313

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 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

Open AccessArticle

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

Cited by 7 | Viewed by 2873

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 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

Open AccessArticle

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

Cited by 1 | Viewed by 1122

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 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

Open AccessArticle

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

Cited by 1 | Viewed by 1486

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 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

Open AccessArticle

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 6 | Viewed by 2220

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 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

Open AccessArticle

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 11 | Viewed by 3143

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 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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Review

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21 pages, 1719 KiB

Open AccessReview

Integrated Micro- and Nano-Grid with Focus on Net-Zero Renewable Energy—A Survey Paper

by Nourin Kadir and Alan S. Fung

Energies 2025, 18(4), 794; https://doi.org/10.3390/en18040794 - 8 Feb 2025

Viewed by 669

Abstract

An integrated micro- and nano-grid with net-zero renewable energy is a sophisticated energy system framework aimed at attaining optimal efficiency and sustainability. This survey paper examines several contemporary research works in this domain. This document summarizes the latest papers selected for analysis to comprehend the current state-of-the-art, integration process, methodology, and research gaps. The objective of this review is to identify existing trends and ongoing transformations in this domain. At the conclusion of the study, emerging technologies for smart grid integration are offered, emphasizing Transactive Control, Blockchain Technology, and Quantum Cryptography, based on existing research gaps. Microgrids and nano-grids are localized energy systems capable of functioning alone or in tandem with larger power grids, offering resilience and adaptability. By incorporating renewable energy sources like solar, wind, and storage devices, these networks can produce and regulate energy locally, guaranteeing that the generated energy meets or surpasses the energy used. The incorporation of intelligent technology and control systems facilitates optimized energy distribution, real-time monitoring, and load balancing, advancing the objective of net-zero energy use. This strategy not only bolsters energy security but also markedly decreases carbon emissions, rendering it an essential element in the shift towards a sustainable and resilient energy future. The worldwide implementation of interconnected micro- and nano-grids utilizing net-zero renewable energy signifies a pivotal transition towards a sustainable and resilient energy future. These localized energy systems can function independently or in conjunction with conventional power grids, utilizing renewable energy sources like solar, wind, and advanced storage technology. Integrating these resources with intelligent control systems enables micro- and nano-grids to optimize energy production, distribution, and consumption at a detailed level, ensuring that communities and companies globally can attain net-zero energy usage. This method not only diminishes greenhouse gas emissions and reliance on fossil fuels but also improves energy security and grid stability in various places. These technologies, when implemented globally, provide a scalable answer to the issues of energy access, environmental sustainability, and climate change mitigation, facilitating a cleaner and more equal energy landscape worldwide. Full article

(This article belongs to the Special Issue Hybrid-Renewable Energy Systems in Microgrids)

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39 pages, 2058 KiB

Open AccessReview

A Framework for Resilient Community Microgrids: Review of Operational Strategies and Performance Metrics

by Joy Dalmacio Billanes, Bo Nørregaard Jørgensen and Zheng Ma

Energies 2025, 18(2), 405; https://doi.org/10.3390/en18020405 - 17 Jan 2025

Cited by 3 | Viewed by 1138

Abstract

The growing frequency of extreme weather events and grid disruptions highlights the urgent need for resilient energy systems. Decentralized and autonomous, community microgrids offer reliable and adaptable solutions. However, existing research often isolates components or control methods, lacking a comprehensive synthesis of their interdependencies and resilience strategies. To address this gap, this study conducts a comprehensive scoping review to synthesize the current state of knowledge on community microgrids, focusing on their types, components, operational strategies, control methods, and performance indicators. The research identifies key microgrid subtypes, such as islanded, hybrid, multi-energy, and autonomous systems, and evaluates the role of critical components like energy storage systems and distributed energy resources in enhancing resilience. It also highlights performance indicators, including reliability, stability, and flexibility, that serve as benchmarks for resilience. A novel framework is proposed, integrating microgrid design and operational aspects into a cohesive model for resilience enhancement. This framework provides actionable insights for practitioners to optimize microgrid design, for policymakers to create adaptive regulations, and for researchers to address knowledge gaps in the field. The findings underscore the critical role of advanced control methodologies in improving adaptability and efficiency under diverse operational conditions. By addressing the lack of an integrated approach in the existing literature, this study contributes to advancing resilient energy systems, supporting the energy transition, and promoting energy security. Future research should validate the proposed framework through empirical studies and explore scalable, cost-effective solutions to enable widespread adoption. Full article

(This article belongs to the Special Issue Hybrid-Renewable Energy Systems in Microgrids)

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