Hybrid Microgrid System Research Articles

Achieving universal energy access in remote locations using HOMER energy model: a techno-economic and environmental analysis of hybrid microgrid systems for rural electrification in northeast Nigeria

The developing world continues to face substantial obstacles to achieving affordable and dependable electricity access. This issue is especially pertinent for Nigeria, where diesel generators are widely relied upon in urban and rural regions because of an underdeveloped and unreliable national grid. The lack of grid reliability is worsened in Northeastern Nigeria, an area plagued by conflict, extreme poverty, and grid infrastructure deterioration. This study investigates the feasibility of implementing community-scale microgrids in rural areas without grid connection access. It focuses on assessing the technical, economic, and environmental aspects of utilizing these microgrids to deliver inexpensive and dependable electricity to underserved populations to increase energy access. A case study was conducted in Kabuiri, a village with an estimated population of 2,300 residents and an estimated load demand of 610 kWh per day. A hybrid microgrid system was designed and optimized to meet the community’s load demand using HOMER software, sized to produce 610 kWh/day of electricity with a renewable penetration of 99%. The optimal solar PV/battery/generator system had a levelized cost of electricity (LCOE) of $ 0.093 per kWh, a net present cost (NPC) of $266,709, and an annual operating cost of $9,110. The system contributed 1,624 kg CO2 eq/year of global warming potential and 56.81 kg O3 eq/year of smog formation during operation. Sensitivity analysis showed that the system could effectively react to or adapt to substantial increases in diesel prices, requiring only marginal increases in photovoltaic capacity and reduced generator usage to maintain the most cost-efficient operation. Additionally, the system model can be adapted based on the population of the remote community without substantially impacting the LCOE, however, the NPC increases with increase in population size. This research will aid in increasing energy access in remote locations by providing insights to stakeholders and energy access project developers.

Experimental investigation of adaptive multi-generalized integrator-based controller for electronically interfaced hybrid microgrid system

This research work explores the conceptualization and evaluation of a hybrid microgrid that taps into the potential of solar photovoltaic systems, wind energy conversion systems, and battery energy storage systems. In practice, nonlinear loads pose power quality issues that significantly impact the performance of hybrid microgrids. To tackle these challenges, an adaptive multi-generalized integrator (MGI) filter is proposed. This filter extracts the fundamental signal from the distorted utility grid voltages. It features a pre-filter with a DC-off set rejection loop, effectively minimizing the DC-offsets from the distorted grid voltages. This facilitates the distribution of active power generated by the hybrid microgrid's sources while simultaneously addressing various power quality problems. In addition, a dynamic power management control approach enhances grid resilience by operating the hybrid microgrid in grid-connected and islanding modes. It provides uninterrupted and high-quality power supply to consumers during grid outages. The performance of this filter is comprehensively assessed through numerical simulations in MATLAB®/Simulink® environment. A real-time laboratory prototype is developed with WAVECT® WUC300 FPGA controller, demonstrating the practical application of the proposed filter. It ensures the minimum DC-offsets of 0.02V, fast convergence, and minimum oscillations. The grid synchronization and power quality index of the grid currents simultaneously achieve a THD of 2.82 %, complying with the IEEE-1547 and IEEE-519 standards. Importantly, the proposed adaptive-MGI filter outperforms the conventional SOGI and LMF filters in terms of nonlinear load tracking capabilities, with a response speed of less than 200μs, thereby highlighting its practical relevance and importance.

English

English

A Techno-Economic Analysis of a Hybrid Microgrid System in a Residential Area of Bangladesh: Optimizing Renewable Energy

In the face of a significant power crisis, Bangladesh is turning towards renewable energy solutions, a move supported by the government’s initiatives. This article presents the findings of a study conducted in a residential area of Pabna, Bangladesh, using HOMER (Hybrid Optimization of Multiple Energy Resources) Pro software version 3.14.2. The study investigates the feasibility and efficiency of a grid-connected hybrid power system, combining photovoltaics (PV), a biomass generator, and wind energy. The simulation produced six competing solutions, each featuring a distinct combination of energy sources. Among the configurations analyzed, the grid-connected PV–biomass generator system emerged as the most cost-effective, exhibiting the lowest COE at USD 0.0232, a total net present cost (NPC) of USD 321,798.00, and an annual operating cost of USD 6060.59. The system presents a simple payback period of 9.25 years, highlighting its economic viability. Moreover, this hybrid model significantly reduces CO2 emissions to 78,721 kg/year, compared to the 257,093 kg/year emissions from a solely grid-connected system, highlighting its environmental benefits. Sensitivity analyses further reveal that the system’s performance is highly dependent on solar irradiance, indicating that slight variations in solar input can significantly impact the system’s output. This study underscores the potential of integrating multiple renewable energy sources to address the power crisis in Bangladesh, offering a sustainable and economically viable solution while also mitigating environmental impacts.

Operational assessment of solar-wind-biomass-hydro-electrolyser hybrid microgrid for load variations using model predictive deterministic algorithm and droop controllers

This study presents the operation and assessment of a solar-wind-biomass-hydro-electrolyser hybrid microgrid for different types of load variations by using a proprietary derivative-free algorithm (Model Predictive Deterministic Algorithm) and voltage IQ droop controller. The proposed hybrid microgrid integrates various renewable energy sources and an electrolyser to generate hydrogen and produce electricity. The objective is to optimize the microgrid’s performance and provide reliable power supply to meet varying load demands. The proprietary derivative-free algorithm is used to determine the optimal dispatch of power from each energy source to meet the load demand while minimizing the overall cost of energy. Additionally, a voltage IQ droop and voltage Q droop controller are employed to regulate the voltage, frequency and active power of the microgrid and maintain its stability during load variations. The proposed hybrid microgrid is assessed under different load scenarios for both grid tied and isolated modes. The results demonstrate that the proposed hybrid microgrid with the derivative-free algorithm and voltage droop controllers can effectively operate and provide reliable power supply under different load variations while maintaining the power system stability of the microgrid. Further, the mutual performances of the controller are compared to find the best controller for the specific microgrid. The findings of this study can contribute to the development of efficient and reliable hybrid microgrid systems for sustainable energy production and distribution.

Evaluation of techno-economic design andimplementation of solar-wind hybrid microgridsystem for a small community

Pakistan is faces significant challenges in meeting its energy demand and consumption needs for consumers. This country's energy production from primary sources such as petroleum and natural gasses is incompetent in fuel-use and hence unable to meet feasibility cost. With an increasing population, Pakistan's energy consumption per capita has been steadily rising. This behaviour is leading to critical energy issues, especially in remote rural areas. This trend in rising energy costs and demand factors are similar to those in the energy markets in the South and South-East Asia. The primary energy sources in Asia continent, including fossil fuels, are insufficient supply to meet this growing demand in production and thus resulting in frequent electricity blackouts. Consequently, renewable energy sources such as solar photovoltaic (PV) and wind power have substantially started to produce energy and to provide a huge portion of Pakistan's daily energy needs apart in conventional energy currently. However, these sources are not yet as reliable, conventional energy bases have a challenge for sustainable energy production. As a result, renewable energy factors nonetheless initial started have effectively stabilized energy consumption, particularly for green electricity with net-zero carbon emissions. The aim of this study is to evaluate the feasibility and cost-effectiveness of integrating a microgrid hybrid system with combined (solar PV/wind power) renewable energy as well as conventional fossil fuel generators. This evaluation focuses on predicting energy production and its costs using Hybrid Optimization of Multiple Energy Resources (HOMER) software, and to enhance the electricity standards at NUST (National University for Sciences and Technology), Pakistan. The proposed methodology of microgrid hybrid system, when evaluated using HOMER software, shows a significant improvement in energy stability and cost efficiency. Moreover, this proposed system can reduce reliance on fossil fuels by a substantial percentage, enhances the predictability of energy production, and optimizes its energy consumption. These can achieve better performance metrics in terms of reliability, cost, and environmental impact; feasible solution for Pakistan and the developing countries. This proposed methodology offers a novel approach by integrating renewable energy sources with conventional generators to create a balanced and efficiency factor by microgrid system. This hybrid system goals as an investigation is to optimize this energy production, reduce carbon emissions, and provide a more stable and cost-effective energy supply.

Harmonized control framework for integrated hybrid microgrid and virtual power plant operation

This paper presents a novel approach in the realm of electric vehicle (EV) applications and renewable energy-based distributed generation (RE-DG) by proposing a unified DC/AC hybrid microgrid (HMG) system tailored for poultry farms. The system aims to streamline design complexity, minimize component requirements, and champion sustainability by seamlessly integrating wind and solar photovoltaic (PV)-based DGs. An innovative EV-based virtual power plant (VPP) concept is introduced to mitigate power intermittency and eliminate the need for energy storage and extra charging stations. To ensure synchronized operation, a Harmonized Control Framework (HCF) is devised, capable of managing both grid-following and grid-forming operations while adapting to dynamic conditions and non-linear loads. Rigorous software and real-time Hardware-in-the-Loop (HIL) testing demonstrate the proposed microgrid's exceptional power transfer capabilities, improved harmonic performance, rapid settling times, and superior voltage/frequency regulation compared to the conventional double-stage synchronous frame (DD-SRF) control approach.

Review of energy management systems and optimization methods for hydrogen‐based hybrid building microgrids

AbstractRenewable energy‐based microgrids (MGs) strongly depend on the implementation of energy storage technologies to optimize their functionality. Traditionally, electrochemical batteries have been the predominant means of energy storage. However, technological advancements have led to the recognition of hydrogen as a promising solution to address the long‐term energy requirements of microgrid systems. This study conducted a comprehensive literature review aimed at analysing and synthesizing the principal optimization and control methodologies employed in hydrogen‐based microgrids within the context of building microgrid infrastructures. A comparative assessment was conducted to evaluate the merits and disadvantages of the different approaches. The optimization techniques for energy management are categorized based on their predictability, deployment feasibility, and computational complexity. In addition, the proposed ranking system facilitates an understanding of its suitability for diverse applications. This review encompasses deterministic, stochastic, and cutting‐edge methodologies, such as machine learning‐based approaches, and compares and discusses their respective merits. The key outcome of this research is the classification of various energy management strategy (EMS) methodologies for hydrogen‐based MG, along with a mechanism to identify which methodologies will be suitable under what conditions. Finally, a detailed examination of the advantages and disadvantages of various strategies for controlling and optimizing hybrid microgrid systems with an emphasis on hydrogen utilization is provided.

Decentralized Smart Energy Management in Hybrid Microgrids: An Evaluation of Operational Modes, Resource Optimization, and Environmental Impacts in Nigeria

Escalating energy demands and climate change challenges necessitate the adaptation of renewable-based microgrid systems in the energy sector. The proposed work employs a robust Multi Agent System (MAS) technique to achieve efficient and automated control of the hybrid microgrid operation. The hybrid microgrid system incorporates Renewable Energy Sources (RES), a diesel generator, and a battery storage system. The operation of the hybrid microgrid consists of three distinct modes: islanded, transition to grid, and grid-oriented mode. The system’s performance is optimized by considering factors like climatic patterns, energy costs, connected source characteristics, and load demand. Different climatic scenarios are assessed for each mode of operation, where the best, extreme sunny, extreme cloudy, and worst climate conditions are considered for islanded mode; sunny and cloudy scenarios are considered for transition to grid mode as well as grid-feed and grid-tied modes are considered for grid-oriented operation of the microgrid. The simulation studies are performed using the MATLAB/Simulink R2021a environment. Furthermore, Particle Swarm Optimization (PSO) is implemented to optimize power allocation within the microgrid and enhance its cost-effectiveness. The optimization results demonstrate efficient utilization of available energy sources along with effective energy management facilitated by the MAS control system. The results emphasize the importance of adopting a MAS approach for achieving smart energy management through comprehensive analysis and integrating decentralized energy management techniques for optimal accommodation of distributed energy resources in hybrid microgrids

Assessing the feasibility and quality performance of a renewable Energy-Based hybrid microgrid for electrification of remote communities

Access to reliable energy is crucial for development, yet many rural areas in southern Bangladesh suffer from electricity shortages, impeding essential services and hindering social and economic progress. This paper proposes integrating renewable energy-based microgrids to provide sustainable and reliable electricity, thereby improving living conditions and boosting economic growth. A detailed survey in Ruma, Bandarban, was conducted for load estimation. Simulation results for on-grid and off-grid microgrids are obtained using HOMER Pro and PVsyst software. The off-grid system includes 21.8 kW of PV, 15 kW of hydro, and 222 kWh of battery storage, while the on-grid system includes a 200 kW PV system and a 15 kW hydro turbine. The levelized cost of energy (LCOE) is 0.15 USD/kWh off-grid and 0.03 USD/kWh on-grid. The on-grid system shows economic sustainability with a 6.8-year break-even point, 13 % IRR, and 8.7 % ROI. Environmental analysis shows significant greenhouse gas reductions, with CO2 emissions decreasing from 227,778 kg/year to 199,016 kg/year. Additionally, a sensitivity analysis is conducted, which underscores the resilience of the proposed hybrid microgrid system to weather variations and cost fluctuations. This paper provides a comprehensive foundation for policymakers to consider renewable microgrids as a solution for rural electrification in southern Bangladesh, utilizing solar and hydropower resources.

Exploring Evolution and Trends: A Bibliometric Analysis and Scientific Mapping of Multiobjective Optimization Applied to Hybrid Microgrid Systems

Hybrid energy systems (HESs) integrate renewable sources, storage, and optionally conventional energies, offering a sustainable alternative to fossil fuels. Microgrids (MGs) bolster this integration, enhancing energy management, resilience, and reliability across different levels. This study, emphasizing the need for refined optimization methods, investigates three themes: renewable energy, microgrid, and multiobjective optimization (MOO), through a bibliometric analysis of 470 Scopus documents from 2010 to 2023, analyzed using SciMAT v1.1.04 software. It segments the research into two periods, 2010–2019 and 2020–2023, revealing a surge in MOO focus, particularly in the latter period, with a 35% increase in MOO-related research. This indicates a shift toward comprehensive energy ecosystem management that balances environmental, technical, and economic elements. The initial focus on MOO, genetic algorithms, and energy management systems has expanded to include smart grids and electric power systems, with MOO remaining a primary theme in the second period. The increased application of artificial intelligence (AI) in optimizing HMGS within the MOO framework signals a move toward more sustainable, intelligent energy solutions. Despite progress, challenges remain, including high battery costs, the need for reliable MOO data, the intermittency of renewable energy sources, and HMGS network scalability issues, highlighting directions for future research.

Realizing smart microgrid electricity solutions for rural communities using a hybrid microgrid system based on renewable energy sources

Realizing smart microgrid electricity solutions for rural communities using a hybrid microgrid system based on renewable energy sources

Techno-economic modeling and optimal sizing of autonomous hybrid microgrid renewable energy system for rural electrification sustainability using HOMER and grasshopper optimization algorithm

This research paper focuses on techno-economic modeling and optimal sizing of autonomous hybrid microgrid systems. The optimal configuration of the suggested stand-alone system was performed by developing a size optimization model based on the metaheuristic novel Grasshopper Optimization algorithm (GOA) method to minimize the Total Net Present Cost (TNPC), unmet load, and Cost of Energy (COE) in the Nsukka Community which comprises 88 villages. The GOA and HOMER Pro Software are employed to compare results across four possible configurations of hybrid renewable power systems (HRES). The comparative analysis between GOA and HOMER shows that configuration-4 (biogas/Diesel), emerges as the optimal solution, with a 0 % unmet load at the COE of $0.01783 per kWh. The findings indicated that the GOA-based HRES, with a higher saturation of Biogas and photovoltaics (PV), proves to be more affordable in comparison to the HOMER-based solutions. The reduction in the COE and NPC of renewable energy for peak demands highlights the growing importance of biogas generators as an affordable local power supply to meet energy demands. This underscores the potential of the GOA in optimizing hybrid renewable energy systems for remote communities, producing an economically viable and sustainable energy solution.

Load management design and techno-economic analysis for an islanded hybrid Pv-Teg microgrid

Integration of various renewable energy sources (RES) is one of the critical factors behind microgrid (MG) capacity development and MG expansion. Distributed energy resources with and without RES, electrical loads, controllers, and storage units constitute the essential components of MGs. On the other hand, with the addition of different power generation sources, load prediction on the user side of the MG becomes an important issue. Demand response solutions and changing end-user behavior are the other sources of uncertainty in the performance of MGs. Therefore, matching supply and demand is a very important issue that must be done immediately at every moment of the operation. A load management system (LMS) in a community MG is essential to ensure the system’s adaptability. Fuzzy logic (FL) controllers and energy management systems will ensure optimum use of energy resources and efficient operation of MG and reduce energy waste. In this study, an 85 kW photovoltaic (PV) and thermoelectric generator (TEG) hybrid MG system (PVTEG-MG), which can operate as an island grid in all regions of the World with hot water resources, has been designed for the first time and its techno-economic analysis has been made. The state of charge (SOC) for the proposed PVTEG-MG remained stable at 40% charge state by adding charge management. The average value of the Levelized Cost of Energy (LCOE) is $0.456/kWh for PV, $0.456/kWh for TEG, and 0.399/kWh over the total energy produced, 219,000 kWh for the PV and 493,650 kWh for the TEG. Overall, this PVTEG-MG system is a promising solution for hot water regions that can be used as an island grid to reduce electricity costs, increase efficiency and reliability, and provide a more sustainable energy source.

A Comprehensive Review of Existing and Pending University Campus Microgrids

Over the past few decades, many universities have turned to using microgrid systems because of their dependability, security, flexibility, and less reliance on the primary grid. Microgrids on campuses face challenges in the instability of power production due to meteorological conditions, as the output of renewable sources such as solar and wind power relies entirely on the weather and determining the optimal size of microgrids. Therefore, this paper comprehensively reviews the university campuses’ microgrids. Some renewable energy sources, such as geothermal (GE), wind turbine (WT), and photovoltaic (PV), are compared in terms of installation costs, availability, weather conditions, efficiency, environmental impact, and maintenance. Furthermore, a description of microgrid systems and their components, including distributed generation (DG), energy storage system (ESS), and microgrid load, is presented. As a result, the most common optimization models for analyzing the performance of campus microgrids are discussed. Hybrid microgrid system configurations are introduced and compared to find the optimal configuration in terms of energy production and flexibility. Therefore, configuration A (Hybrid PV- grid-connected) is the most common configuration compared to the others due to its simplicity and free-charge operation.

Instantaneous power theory based an improved LVRT strategy for PV-PEMFC based hybrid micro-grid system

Instantaneous power theory based an improved LVRT strategy for PV-PEMFC based hybrid micro-grid system

Maximizing hybrid microgrid system performance: A comparative analysis and optimization using a gradient pelican algorithm

The development of hybrid systems that combine different energy sources has become increasingly important to meet the growing demand for clean and sustainable energy. These systems can offer a reliable and efficient solution to reduce dependence on conventional energy sources and address the issues of climate change and environmental degradation. This study focuses on two types of hybrid systems, namely PV/biomass and PV/diesel, which are commonly used in off-grid areas or places with limited access to electricity. The proposed optimization framework aims to determine the optimal sizing of each component in the system, including PV panels, batteries, and backup sources, to maximize the performance and efficiency of the hybrid system. The Gradient Pelican Optimization Algorithm (GPOA), which is an advanced optimization technique, is proposed to solve the optimization problem. The algorithm is modified to include a local escaping operator, which allows the algorithm to escape local optima and achieve better solutions. The outcomes of the simulation show that the GPOA algorithm outperforms other well-known algorithms in terms of accuracy and computational efficiency. The simulation results also demonstrate that the PV/biomass hybrid system outperforms the PV/diesel hybrid system in terms of cost and environmental impact. The proposed framework and optimization methodology can be useful for designing and optimizing other hybrid systems and can be applied to various applications, including off-grid and remote areas, distributed power systems, and microgrids. The findings of this study can also contribute to the development and promotion of renewable energy and sustainable development.

Fault Prediction and Awareness for Power Distribution in Grid Connected RES Using Hybrid Machine Learning

This study addresses defects in electrical power systems, focusing on short circuits that can disrupt normal operation. The method emphasis is on hybrid microgrid systems connected to the grid, crucial for efficient power management. The novel method proposes an adaptive electricity management technique for fault scenarios in grid-tied conversions, minimizing sensor requirements. Power flow involves the hybrid energy storage system (HESS), renewable energy sources (RES), the electrical grid, and loads through a parallel hybrid RES arrangement. Semiconductor switch failures are common vulnerabilities, impacting grid functionality. The novel electricity management technique considers converter failures, renewable energy availability, and HESS state of charge restrictions. Power quality goals such as harmonic reduction, reactive power support, and a power factor of one are addressed. The proposed method was evaluated using MATLAB–based Simulink, showcasing its performance. The approach employs an Adaptive Power Management Strategy (APMS) in a hybrid renewable energy system, integrating wind and solar sources with battery and supercapacitor storage. A Hybrid Machine Learning-based Active Power Management System (HML-APMS) optimizes energy production, storage, and distribution for stability, reliability, and efficiency in the power utility system. The study underscores the significance of technology integration in achieving sustainable, reliable, and cost-effective power solutions.

Feasibility and sustainability analysis of a hybrid microgrid in Bangladesh

The demand for renewable sources-based micro-grid systems is increasing all over the world to address the United Nation’s (UN) sustainable development goal 7 (SDG7) “affordable and clean energy”. However, without proper viability analysis, these micro-grid systems might lead to economic losses to both customers and investors. Therefore, this paper aims to explore the feasibility and sustainability of a hybrid micro-grid system based on available renewable resources in remote hill tracts region of Bangladesh. Nine different scenarios are analyzed here, and a combination of solar, hydro, biogas, and diesel generator systems are found to be the best feasible solution in regard to the least cost of electricity and emission. The optimized result shows that with a renewable fraction of 0.995, the unit levelized cost of energy of the micro-grid system is $0.182 and it emits 54 and 117 times less CO<sub>2 </sub>compared to grid-based and diesel-based systems. Further, the fuel share of the system being 0.5% and greenhouse gas per energy being 0.06425 kg/KWh, validate the system as highly sustainable and eco-friendly. With the ability to fulfill load demands without interrupting supply, and reducing the emissions of greenhouse gases, the designed microgrid can provide sustainable energy solutions to any hill-tracts of Bangladesh.

Enhancing Sustainability in Hopedale, Newfoundland and Labrador, Through Hybrid Microgrid System Design

Enhancing Sustainability in Hopedale, Newfoundland and Labrador, Through Hybrid Microgrid System Design