Hybrid Energy System Research Articles

Hybrid Renewable Energy System in an Isolated Region of Brazil: Evaluation of Impacts on Employment Indices, HDI, and Energy Efficiency

Theoretical Framework: The electrification of isolated communities, such as the Lago do Cuniã Extractive Reserve in Rondônia, requires sustainable energy solutions that promote social development. Although studies of renewable hybrid systems focus on economic optimization, there is a gap in the literature on their impacts on social indicators such as the Human Development Index (HDI) and job creation. This study aims to fill this gap by investigating the social effects of a hybrid energy system in the Brazilian Amazon. Method: An exploratory-descriptive, quantitative study was conducted to design a hybrid energy generation system for the community of Lago do Cuniã. The iHOGA software was used for energy simulations and NASA POWER for solar resource analysis. The proposed system integrates solar panels, diesel generators, batteries, an electrolyzer, an inverter, and a fuel cell. The methodology included energy resource assessment, component optimization, and calculation of the HDI and jobs generated. The employability analysis considered references from the literature (Lujano-Rojas et al., 2016; Rojas-Zerpa and Yusta, 2015). The HDI was calculated for a population of 400 people based on energy demand. Results and Discussion: The simulations indicated that the diesel generator had the greatest impact on job creation. However, a 75% increase in the community's energy demand, attributed to income growth and equipment acquisition, raised the HDI from 0.46 to 0.52 (an increase of 0.06). The optimized hybrid system produced 112,997 MWh/year. Replacing 3.84 MWh/year of diesel energy with solar energy would result in a reduction of approximately 5.36 tons of CO₂ per year (52.3%) and annual savings of about US$ 39,223. Research Implications: This study demonstrates that optimizing hybrid systems with a priority on renewable energy can increase the economic attractiveness of the project and boost human development by reducing the use of fossil fuels. The findings are crucial for planning sustainable rural electrification and promoting balanced local development. Originality/Value: The originality lies in the assessment of the impacts of a hybrid energy system on social (employability and HDI) and environmental (CO₂ reduction) indices in an isolated community in the Amazon, a topic that has been little explored in the literature. The comparative analysis of scenarios and the quantification of social and economic benefits add practical and theoretical value to the field.

An Overview of Current Optimization Approaches for Hybrid Energy Systems Combining Solar Photovoltaic and Wind Technologies

ABSTRACTThis study reviews recent developments in optimization techniques for hybrid solar photovoltaic and wind energy systems, particularly those using artificial intelligence (AI) and hybrid algorithms. Due to the global need for sustainable energy, the study compares both traditional and modern optimization techniques. It shows that hybrid algorithms, like, Gray Wolf–Cuckoo Search Optimization (GWCSO), can speed up convergence and reduce costs by up to 25% compared with other conventional methods, such as linear programming. The study groups optimization techniques into traditional, software‐based, AI‐driven, and hybrid approaches; assessing how well they improve system efficiency, reliability, and cost. It also outlines sizing methods and their economic, technical, and environmental effects, with results showing that AI‐driven methods can lower the levelized cost of energy by 10%–15% in complex microgrids (MGs). The study further provides a structured way to size MGs, addressing a gap in optimization methods for independent hybrid systems in remote locations. Greater flexibility of hybrid algorithms in handling complex optimization problems was emphasized. Ultimately, this study offers new insights into combining AI with traditional methods, suggesting future research directions for both smart grid and MG design.

Analysis and Modeling of a Grid-Connected Hybrid Microgrid Utilizing Wind, Solar, and Fuel Cell Technologies

The widespread reliance on conventional energy sources such as coal, oil, and natural gas has driven the focus toward developing renewable energy alternatives. Renewable sources like solar and wind energy are mature, cost-effective, and widely utilized. Additionally, fuel cell technology has reached an advanced stage of development. These energy sources are not only abundant and cost-free but also environmentally friendly. Combining these resources leads to the creation of a hybrid energy system. The proposed hybrid system, integrating solar energy, wind energy, and fuel cells, is highly effective for distributed energy production.

Multi-objective optimal performance of a distribution system based on integrating renewable energy sources and hybrid energy systems

Multi-objective optimal performance of a distribution system based on integrating renewable energy sources and hybrid energy systems

Techno-economic and environmental optimization of hydrogen-based hybrid energy systems for remote off-grid australian communities

Techno-economic and environmental optimization of hydrogen-based hybrid energy systems for remote off-grid australian communities

An innovatively designed community-based hybrid energy system to generate its needs of electricity, heat, hot water and hydrogen in a sustainable manner

An innovatively designed community-based hybrid energy system to generate its needs of electricity, heat, hot water and hydrogen in a sustainable manner

Long-Term and Short-Term Coordinated Scheduling for Wind-PV-Hydro-Storage Hybrid Energy System Based on Deep Reinforcement Learning

Long-Term and Short-Term Coordinated Scheduling for Wind-PV-Hydro-Storage Hybrid Energy System Based on Deep Reinforcement Learning

Stochastic sizing and energy management of a hybrid energy system using cloud model and improved Walrus optimizer for China regions

This paper presents a new stochastic-intelligent framework for sizing and energy management of a hybrid renewable energy system consisting of photovoltaic (PV), wind turbine, and hydrogen energy storage-based fuel cells (PV/Wind/FC). The framework incorporates a cloud model to address uncertainties in renewable generation and system load, with aim of the cost of energy (COE) while satisfying the loss of energy probability (LOEP). An improved Walrus Optimizer (IWO) with a piecewise linear chaotic map is applied to determine the optimal system component sizes. The model’s effectiveness is evaluated through deterministic and stochastic scenarios using real meteorological data from Beijing, Guangzhou, Kashi, and Xining, China. The deterministic results clear that the PV/Wind/FC system outperforms other configurations, achieving the lowest COE and LOEP. The COE values for Beijing, Guangzhou, Kashi, and Xining are 0.260, 0.202, 0.246, and 0.217 $/kWh, respectively. The IWO algorithm demonstrates superior performance compared to traditional methods such as WO, PSO, MRFO, and GWO in terms of COE, reliability, convergence speed, and stability. In the stochastic approach based on cloud model, the COE increases by 13.84%, 14.85%, 10.97%, and 15.66% for the respective regions, highlighting the impact of renewable generation and system demand uncertainties. Additionally, the cloud model findings demonstrate how uncertainty distributions impact the system’s operation, with the variation in cloud model droplets on both sides of the expected value reflecting the effects of renewable generation and demand uncertainties. This provides a more comprehensive and reliable framework for HRES design under uncertain conditions compared to the deterministic model.

Dynamic optimization of a solar–sCO2–CAES hybrid energy system: Integration of green hydrogen production and AI-based energy management

Dynamic optimization of a solar–sCO2–CAES hybrid energy system: Integration of green hydrogen production and AI-based energy management

Multi-objective optimization of operational strategy and capacity configuration for hybrid energy system combined with concentrated solar power plant

Multi-objective optimization of operational strategy and capacity configuration for hybrid energy system combined with concentrated solar power plant

Optimal design and sizing of energy storage solution-based hybrid energy system considering economic and technical objective functions: A case study

Optimal design and sizing of energy storage solution-based hybrid energy system considering economic and technical objective functions: A case study

Renewable energy conversion systems for global emission neutralization

Fossil fuel power plants still play an essential role in providing energy worldwide, but their environmental impact will contribute significantly to emissions and environmental pollution. To reduce these emissions, renewable energy offers a solution to reduce global emissions. This study proposes a renewable energy modeling system using hybrid optimization of multiple energy resources (HOMER) simulation on renewable energy systems for economic savings. This simulation can combine photovoltaic (PV), wind power (WP), and converter systems. The hybrid combination of PV and WP is the most appropriate and economical choice at the research location. The results showed that the modeling of the renewable energy hybrid system made a significant contribution, with an initial investment cost of IDR 107,474.43 million and an annual operating cost of IDR 22,540.23 million, 41% lower on condition now with an estimated return on investment of 11 years. The results of this study can be used as recommendations for similar conditions in other places. Policymakers can use this model to provide incentives and have a positive impact on hybrid power plants (HPS) in neutralizing global emissions.

Optimizing MPPT Extraction in Hybrid Energy Systems Using an Adaptive PSO Topology

In both solar and wind energy systems, Maximum Power Point Tracking (MPPT) plays a crucial role in maximizing energy extraction and optimizing overall system efficiency. The concept of MPPT revolves around continuously adjusting the operating conditions of the renewable energy source to ensure that it operates at its MPP under varying environmental conditions. In solar photovoltaic (PV) systems, MPPT is essential due to the non-linear relationship between the voltage and current output of solar panels. This non-linearity arises from factors such as temperature variations, shading, and changes in solar irradiance levels throughout the day. Without proper MPPT control, solar panels may operate below their MPP, leading to significant energy losses. MPPT algorithms play a pivotal role in optimizing the efficiency of photovoltaic (PV) systems by continuously tracking and extracting maximum power from the solar panels. Among various MPPT techniques, Particle Swarm Optimization (PSO) has emerged as a promising approach due to its simplicity and effectiveness in dealing with the nonlinear and dynamic nature of PV systems. Similarly, in wind energy systems, MPPT is critical for maximizing power generation from wind turbines. Wind speed variations, turbulence, and changes in wind direction can cause fluctuations in the output power of wind turbines. Particle Swarm Optimization (PSO) topology adjusts the rotor speed or blade pitch angle to ensure that the turbine operates at its optimal rotational speed, thereby extracting the maximum available wind energy.

Optimization of Solar and Wind Hybrid Energy System with IoT Integration for Remote Areas in Indonesia

Limited access to electricity in remote areas of Indonesia reflects complex structural issues, including geographic constraints, policy inequalities, and the low effectiveness of centralized approaches. Thousands of villages in the 3T (Disadvantaged, Frontier, and Outermost) regions still do not enjoy reliable electricity, hampering socio-economic development and reinforcing the cycle of poverty. Although Indonesia has great potential for renewable energy—such as solar and wind—its use is still minimal due to infrastructure constraints, investment, and non-contextual development approaches. In this context, a hybrid solar-wind energy system integrated with Internet of Things (IoT) technology offers an efficient and sustainable decentralized solution. IoT enables real-time monitoring, predictive maintenance, and data-driven energy management, which are critical in remote areas with limited technicians and physical access. This study uses a qualitative approach through a literature review to examine the challenges, potential, and policy relevance in developing IoT-based renewable energy systems. The results of the analysis show that transforming energy systems in marginalized areas requires not only technological innovation, but also a paradigm shift towards inclusive energy justice. Therefore, strengthening local capacity and integrating context-based policies are key to realizing a fair, reliable and sustainable energy system in Indonesia.

Integrating lean maintenance and smart monitoring to enhance energy efficiency in hybrid solar-mechanical systems

This article examines the integration of lean maintenance methodologies with smart monitoring technologies to optimize energy efficiency in hybrid solar-mechanical systems across the United States. Through empirical analysis and case studies, we demonstrate how principles such as 5S, Kaizen, and PDCA can be effectively combined with IoT sensors, machine learning algorithms, and predictive analytics to reduce waste, minimize downtime, and maximize energy yield. Our findings indicate that integrated approaches can achieve energy efficiency improvements of 15-27% compared to traditional maintenance regimes, with corresponding reductions in operational costs of 18-32%. The research highlights the synergistic relationship between process optimization and technological innovation, providing a framework for implementation that is adaptable across various scales and configurations of hybrid energy systems.

A high-efficiency hybrid energy system composed of fuel cell and thermoelectric generator for light electric vehicles

This study proposes a more effective utilization of fuel cell (FC) technology by integrating a thermoelectric generator (TEG), which harnesses waste heat to generate electrical energy. While the FC still requires hydrogen, the integration of a TEG enables supplementary power generation from waste heat without any additional fuel input, resulting in a more efficient and compact hybrid system. The Perturb and Observe (P&O) MPPT-based SEPIC converter is highlighted for use in light fuel cell electric vehicle (FCEV) applications due to its inherent buck–boost capability, making it particularly suitable for practical implementations. To achieve maximum efficiency and ensure stable operation of the TEG, the FC is operated at its nominal power of 93.75 W in this system. The integrated TEG system, combined with a high-efficiency boost converter, contributes approximately 5-W to the proposed hybrid energy system, which achieves an overall efficiency of 96.1%. The proposed hybrid energy system holds great potential for providing sustainable energy solutions in transportation applications.

INVESTIGATING THE RECONSTRUCTION OF RENEWABLE ENERGY PRODUCTION AND RECREATIONAL INFRASTRUCTURE IN NABRAN RECREATIONAL ZONE

This study explores the integration of renewable energy solutions in the reconstruction of the Nabran recreational zone’s infrastructure. The region, known for its coastal tourism, faces increasing energy demands and environmental concerns. By analyzing the feasibility of solar, wind, and hybrid energy systems, this study assesses their potential to enhance sustainability while improving economic and environmental conditions. The research findings suggest that implementing renewable energy technologies in tourism infrastructure can significantly reduce the carbon footprint, lower operational costs, and attract eco-conscious visitors. The results also highlight the importance of government incentives and strategic planning to ensure successful implementation.

Approaching a Nearly Zero Energy Building Integrated with PCM by Optimization of Energy Sources

In recent years, population growth, the enhancement of carbon emissions generation, and higher energy consumption have caused the movement to nearly zero-energy buildings. Additionally, the various strategies, phase change materials (PCMs) are suitable for reducing the energy consumption of a building. The focus of this study is to investigate the results of three scenarios that explore all the effective parameters for selecting a suitable Phase Change Material (PCM) for hot climate conditions in Saudi Arabia. The first scenario worked on choosing the best phase change material based on the climatic conditions and the selected area. To complete the optimization, the best thickness and placement of the two-layer phase change material were investigated in the second and third scenarios. The results indicated that optimized building using PCM 29 with 50 mm thickness reduced the energy consumption and carbon dioxide production by 20.72% and 21.05%, respectively. Furthermore, the outcomes of the study on two-layer phase change materials with different arrangements illustrated that the most proper location of PCMs caused 255.38 MWh of electricity consumption and 155.71 × 103 kg of carbon dioxide production. Finally, as a novel integration, the results of using one-layer and two-layer PCM were added to the HOMER software to find the optimal hybrid energy systems. The findings showed that by integrating photovoltaic panels, diesel generation, batteries, and the grid, the cost of energy reached USD 0.162. Additionally, the grid purchase by using one-layer and two-layer phase change material was decreased by 21.2% and 24.3% compared to the base case.

Hybrid Energy-Powered Electrochemical Direct Ocean Capture Model

Offshore synthetic fuel production and marine carbon dioxide removal can be enabled by direct ocean capture, which extracts carbon dioxide from the ocean that then can be used as a feedstock for fuel production or sequestered underground. To maximize carbon capture, plants require a variety of low-carbon energy sources to operate, such as variable renewable energy. However, the impacts of variable power on direct ocean capture have not yet been thoroughly investigated. To facilitate future deployments, a generalizable model for electrodialysis-based direct ocean capture plants is created to evaluate plant performance and electricity costs under intermittent power availability. This open-source Python-based model captures key aspects of the electrochemistry, ocean chemistry, post-processing, and operation scenarios under various conditions. To incorporate realistic energy supply dynamics and cost estimates, the model is coupled with the National Renewable Energy Laboratory’s H2Integrate tool, which simulates hybrid energy system performance profiles and costs. This integrated framework is designed to provide system-level insights while maintaining computational efficiency and flexibility for scenario exploration. Initial evaluations show similar results to those predicted by the industry, and demonstrate how a given plant could function with variable power in different deployment locations, such as with wind energy off the coast of Texas and with wind and wave energy off the coast of Oregon. The results suggest that electrochemical systems with greater tolerances for power variability and low minimum power requirements may offer operational advantages in variable-energy contexts. However, further research is needed to quantify these benefits and evaluate their implications across different deployment scenarios.

Integrating Hybrid Energy Solutions into Expressway Infrastructure

To explore the feasibility of renewable hybrid energy systems for expressway infrastructure, this study proposes a scenario-based design methodology integrating solar, wind, and hydropower resources within the expressway corridor. A case study was conducted on a highway service area located in southern China, where a solar/wind/hydro hybrid energy system was developed based on the proposed approach. Using the HOMER Pro 3.14 software platform, the system was simulated and optimized under off-grid conditions, and a sensitivity analysis was conducted to evaluate performance variability. The results demonstrate that the strategic integration of corridor-based natural resources—solar irradiance, wind energy, and hydrodynamic potential—enables the construction of a technically and economically viable hybrid energy system. The system includes 382 kW of PV, 210 kW of wind, 80 kW of hydrokinetic power, a 500 kW diesel generator, and 180 kWh of battery storage, forming a hybrid configuration for a stable and reliable energy supply. The optimized configuration can supply up to 1,095,920 kWh of electricity annually at a minimum levelized cost of energy of USD 0.22/kWh. This system reduces CO2 emissions by 23.2 tons/year and NOx emissions by 23 kg/year. demonstrating strong environmental performance and long-term sustainability potential.