Hybrid Renewable Energy System Research Articles

Multilevel stacked deep learning assisted techno-economic assessment of hybrid renewable energy system

Multilevel stacked deep learning assisted techno-economic assessment of hybrid renewable energy system

Feasibility Assessment of Hybrid Renewable Energy Based EV Charging Station in Libya

This study presents an assessment of the feasibility of implementing a hybrid renewable energy-based electric vehicle (EV) charging station at a residential building in Tripoli, Libya. Utilizing the advanced capabilities of HOMER Grid software, the research evaluates multiple scenarios involving combinations of solar and wind energy sources integrated with energy storage and the utility grid. This analysis provides a novel approach to enhancing urban energy systems with renewable technologies in a region traditionally reliant on fossil fuels. Key contributions of this study include the demonstration of an innovative integration strategy that combines solar and wind power with battery storage to ensure a reliable and efficient energy supply for EV charging. Furthermore, the study addresses the practical implications for local energy policy, suggesting that such hybrid systems can significantly enhance energy security and support sustainable urban development. The authors studied five scenarios using HOMER. The results reveals that the annual total costs and payback periods are as follows: for Scenario 1 (wind/utility grid), the expenditure totals US$1,554,416 and payback period of 4.8/5.8 years; for Scenario 2 (solar/wind/Utility grid), the amount is US$1,554,506 and payback period of 4.8/5.8 years; and for Scenario 3(solar/wind/storage/utility grid), it escalates slightly to US$1,554,731, all predicated on the utility grid tariffs and payback period of 4.8/5.8 years. Furthermore, in Scenario 4 (solar/utility grid), the annual total cost is significantly reduced to US$30,589 and a payback period of 8.1/14.3 years, while Scenario 5 (solar/storage/utility grid) incurs an even lower expenditure of US$28,572, again based on the utility grid tariffs and a payback period of 14.0 years. The findings contribute valuable insights into the scalability and adaptability of renewable energy solutions, providing a robust framework for policymakers and planners considering similar implementations in other regions. Overall, the research underscores the potential of integrated renewable energy systems to transform urban energy infrastructures, promoting a sustainable and resilient energy future. The HOMER Grid analysis shows that configurations with energy storage are more cost-effective in the long run, even though they require higher initial costs. It also offers important insights into the economic viability and optimization of hybrid renewable energy systems for an EV charging station in Tripoli, Libya. These results highlight the significance of making calculated investments in renewable energy infrastructure and supporting policies for the development of sustainable energy.

Multi-Objective Optimization of Insulation Thickness with Respect to On-Site RES Generation in Residential Buildings

This paper investigates the optimization of insulation thickness with respect to the integration of renewable energy systems in residential buildings in order to improve energy efficiency, maximize the contribution of renewables and reduce life cycle costs. Using the DesignBuilder and EnergyPlus software, this study models a representative two-story residential building located in Athens, Greece. The building envelope features extruded polystyrene thermal insulation and windows with unplasticized polyvinyl chloride frames and low-e glazing. Six scenarios with hybrid renewable energy systems are analyzed, including air- and ground-source heat pumps, solar thermal systems and a biomass fired boiler, so as to assess energy consumption, economic feasibility and internal air temperature conditions. A Pareto-fronts-based optimization algorithm is applied to determine the optimal combination of insulation thicknesses for the walls, the roof and the floor, focusing on minimizing the life cycle cost and maximizing the percentage of renewable energy utilized. The results demonstrate that scenarios involving biomass boilers and solar thermal systems, both for heating and cooling, when combined with reasonable thermal protection, can effectively meet the recent European Union’s directive’s goal, with renewable energy systems contributing more than 50% of the total energy requirements, whilst maintaining acceptable internal air temperature conditions and having a life cycle cost lower than contemporary conventional buildings.

Optimizing power output in hybrid photovoltaic/wind systems: a nonlinear back-stepping approach for enhanced efficiency and stability

Abstract This paper investigates the challenge of controlling hybrid renewable energy systems (HRES), specifically those combining wind energy and photovoltaic sources, under varying environmental conditions such as fluctuating wind speeds and partial shading. The primary objective is to develop a robust backstepping control strategy that enhances the system’s stability and energy efficiency while ensuring seamless grid integration through the use of dual-fed induction generators. The study uses advanced modeling techniques, including maximum power point tracking for wind turbines and particle swarm optimization for photovoltaic systems, to optimize energy capture. A detailed simulation framework was designed to validate the effectiveness of the control strategy under different climatic scenarios. Quantitative results show that the wind turbine achieved over 95% power recovery, the DC link voltage remained stable within 0.5% of the reference, and photovoltaic energy extraction was optimized with 98% accuracy, even under partial shading. These findings indicate that the proposed control strategy significantly enhances the performance, reliability, and adaptability of the HRES. This work offers a promising contribution to the integration of renewable energy sources into the electrical grid, supporting a more sustainable energy future.

A Comprehensive Review of Remaining Useful Life Estimation Approaches for Rotating Machinery

This review paper comprehensively analyzes the prognosis of rotating machines (RMs), focusing on mechanical-flaw and remaining-useful-life (RUL) estimation in industrial and renewable energy applications. It introduces common mechanical faults in rotating machinery, their causes, and their potential impacts on RM performance and longevity, particularly in wind, wave, and tidal energy systems, where reliability is crucial. The study outlines the primary procedures for RUL estimation, including data acquisition, health indicator (HI) construction, failure threshold (FT) determination, RUL estimation approaches, and evaluation metrics, through a detailed review of published work from the past six years. A detailed investigation of HI design using mechanical-signal-based, model-based, and artificial intelligence (AI)-based techniques is presented, emphasizing their relevance to condition monitoring and fault detection in offshore and hybrid renewable energy systems. The paper thoroughly explores the use of physics-based, data-driven, and hybrid models for prognosis. Additionally, the review delves into the application of advanced methods such as transfer learning and physics-informed neural networks for RUL estimation. The advantages and disadvantages of each method are discussed in detail, providing a foundation for optimizing condition-monitoring strategies. Finally, the paper identifies open challenges in prognostics of RMs and concludes with critical suggestions for future research to enhance the reliability of these technologies.

Hybrid solar photovoltaic-wind turbine system for on-site hydrogen production: A techno-economic feasibility analysis of hydrogen refueling Station in South Korea's climatic conditions

The South Korean government has an ambitious policy to roll out hydrogen-powered vehicles across the country with 2000 hydrogen refueling stations (HRSs) infrastructure by 2050. However, the country currently lacks sufficient HRS infrastructure. In this context, this study proposes and investigates the technoeconomic feasibility and performance assessment of an optimal hybrid renewable energy system integrated with a vanadium redox flow battery for on-site hydrogen production. The system is designed to refuel a fleet of 20 fuel cell electric vehicles at seven South Korean locations with distinct climates. Based on the typical daily hydrogen and electric load profile of the HRS, the capital expenditure, net present cost, operation expenditure, levelized cost of hydrogen and levelized cost of energy were estimated to be, $5.95 - $13.2 M, $8.93 - $19.4 M, 82,670–202,184 $/yr, 8.77–19.1 $/kg and 2.1–4.58 $kWh, respectively.

Design of PV Battery Hybrid Inverter.

This research explores a hybrid renewable energy system integrating solar and additional renewable sources with energy storage for a resilient, eco-friendly power solution. Through adaptive control algorithms, the system autonomously optimizes energy flow based on environmental and grid conditions, ensuring uninterrupted power. Simulations under varying demand scenarios demonstrate enhanced reliability and a reduced carbon footprint. The findings highlight the system's potential as a sustainable model for future energy infrastructure.

Multi-agent based optimal sizing of hybrid renewable energy systems and their significance in sustainable energy development

This paper delves into the enhancement and optimization of on-grid renewable energy systems using a variety of renewable energy sources, with a particular focus on large-scale applications designed to meet the energy demand of a certain load. As global concerns surrounding climate change continue to mount, the urgency of replacing traditional fossil fuel-based power generation with cleaner, more cost-effective and dependable alternatives becomes increasingly apparent. In this context, a comprehensive investigation is conducted on grid connected hybrid energy system that combines photovoltaic, wind, and fuel cell technologies. The study employs three state-of-the-art optimization algorithms, namely Walrus Optimization Algorithm (WaOA), Coati Optimization Algorithm (COA), and Osprey Optimization Algorithm (OOA) to determine the optimal system size and energy management strategies, all aimed at minimizing the cost of energy (COE) for grid-based electricity. The results of the optimization process are compared with the results obtained from the utilization of the Particle swarm optimization (PSO) and Grey Wolf optimizer (GWO). The findings of this study underscore both the practical feasibility and the critical importance of adopting on-grid renewable energy systems to decrease the dependence on traditional energy sources within the grid. The proposed WaOA succeeded to reach the optimal solution of the optimal design process with a COE of 0.51758129611 $//kwh while keeping the loss of power supply probability (LPSP), the reliability index, at 7.303681e-19. The practical recommendations and forward-looking insights provided within this research hold the potential to foster sustainable development and effectively mitigate carbon emissions in the future.

Techno-economic analysis of hybrid renewable energy systems for cost reduction and reliability improvement using dwarf mongoose optimization algorithm

The global energy crisis, particularly in isolated and remote regions, has increased interest in renewable energy sources (RESs) to meet growing energy demands. Integrating RESs with energy storage systems offers a promising solution to mitigate fluctuations and intermittency, but concerns about cost and reliability remain. This study explores the optimal design of various microgrid configurations, combining photovoltaic (PV), wind turbine (WT), battery energy storage system (BESS), and diesel generator (DG) systems for Najran city, Saudi Arabia, via real-world meteorological and load demand data. The Dwarf Mongoose Optimization Algorithm (DMOA), alongside the salp swarm algorithm (SSA) and whale optimization algorithm (WOA), was applied to minimize the levelized cost of energy (LCOE) while improving system reliability. The results demonstrate that the PV/BESS configuration, although cost-effective with an LCOE of 0.038 USD/kWh, fail to meet reliability constraints with a loss of power supply probability (LPSP) of 0.679. In contrast, the PV, WT, BESS, and DG configurations achieved an LPSP of 1.9 × 10^--8% with an LCOE of 0.199 USD/kWh, offering a robust and reliable solution for the region's energy needs. This paper presents a novel application of the DMOA for optimizing hybrid renewable energy systems, demonstrating its effectiveness in achieving a balance between cost and reliability. This strategy provides a viable approach for sustainable energy planning in similar regions facing energy challenges.

Hybrid renewable energy systems for sustainable power supply in remote location: Techno-economic and environmental assessment

Electricity supply is inconsistent and unreliable in many remote areas of India, where depending solely on a single renewable energy source is impractical. In this context, this study investigates the potential of off-grid hybrid renewable energy systems (HRES) to meet the energy needs of a village community in India. Techno-economic analysis and life cycle assessment have been employed to compare eleven HRES combinations which combine photovoltaic (PV), wind turbine (WT), battery (BAT), diesel generator (DG), biogas generator (BG), converter (CONV), and electrolyser (ELEC). By optimising the size and capacity of each component in HRES, this study aims to identify the combination with the lowest levelised cost of electricity (LCOE). This research aligns with United Nations Sustainable Development Goal No. 7 to seek “Affordable and Clean Energy”. The findings highlight that HRES comprising PV/WT/BAT/CONV/DG exhibits the lowest LCOE (0.319 $/kWh) and net present cost (6.81 M$) among all combinations. In systems with partial reliance on diesel, integrating both PV and WT could reduce diesel consumption and increase the renewable fraction to 87.4 %. For HRES involving PV, a significant contribution to greenhouse gas emissions occurs during the construction stage. The WT/DG combination, with its high diesel dependency, has the largest global warming potential. The efforts from this study provide valuable insights into determining the optimal HRES for remote communities by considering their economic and environmental factors.

Optimal planning for hybrid renewable energy systems under limited information based on uncertainty quantification

Developing hybrid renewable energy systems (HRES) is crucial for ensuring affordable, sustainable, and reliable energy access in remote rural and island regions. Rich and detailed data play a pivotal role in the optimal design of HRES. However, remote areas often face data limitations due to inadequate metering infrastructure, which further increases uncertainties in HRES design. This paper proposes a planning optimization method that quantifies multiple uncertainties, requiring only limited information for the optimal HRES design. First, uncertainties in HRES planning are classified into different types based on intrinsic randomness, data availability, and impact level. Then, by integrating uncertainty quantification with credibility theory, knowledge-based scenario generation models are developed to leverage limited available information to generate extreme and typical operating conditions for a tailored HRES design. Finally, a fuzzy credibility-constrained stochastic-robust optimization model is proposed to optimize the design of HRES. A practical case facing significant data scarcity and gaps is used to validate the effectiveness of the proposed method. The proposed approach reduces the loss of load probability for HRES to 0.02% and lowers the levelized cost of energy to $0.16/kWh—33% lower than that of the deterministic method and 79% lower than that of the traditional stochastic method.

Performance analysis of hybrid off-grid renewable energy systems for sustainable rural electrification

Delivering sustainable power to rural communities in sub-Saharan Africa, particularly in Somalia, where many areas are far from the electrical grid and suffer from energy shortages, necessitates the deployment of appropriate technologies. This study evaluates the techno-economic and environmental viability of a hybrid renewable energy system (HRES) comprising a 15 kWp photovoltaic (PV) generator, 10 kW wind turbine (WT), 25 kW diesel generator (DG), and a 72-kWh battery energy storage system (BESS). The HRES is proposed to supply sustainable power to a rural community in Somalia. The techno-economic assessment of the proposed HRES and a comparative analysis of other configurations—namely the PV/WT/BESS and PV/DG/BESS systems is conducted using HOMER and MATLAB software. The results indicate that the proposed PV/WT/DG/BESS HRES demonstrates superior techno-economic performance compared to the other configurations. The lowest net present cost of $96,899.16 and the levelized cost of energy (LCOE) of $0.090/kWh were achieved. This system attains 25 % excess electricity, maintains a 0 % unmet electric load, and demonstrates robust environmental performance with a renewable penetration rate of 91.8 %. Approximately 53.34 % of greenhouse gas emissions are reduced compared to the PV/DG/BESS configuration. The study confirmed the significant impacts of all sensitivity parameters on operational costs, LCOE, fuel prices, fuel intake, and renewable fraction. The overall results indicate superior performance in the optimal system case, offering a feasible and suitable choice for the sustainable electrification of rural communities.

Optimization Scheduling of Off-grid Hybrid Renewable Energy Systems Based on Dung Beetle Optimizer with Convergence Factor and Mathematical Spiral

With the rapid development of renewable energy and the increasing modernization demands in remote areas, off-grid hybrid renewable energy systems (HRES) have become a key technology for achieving sustainable development. This paper presents an improved Dung Beetle Optimization (DBO) algorithm that enhances step size by introducing six elementary functions as convergence factors. It combines polar coordinate expressions of three different mathematical spirals, multiplied by a zeroing factor related to the number of iterations, resulting in six distinct mathematical images that optimize the algorithm's dancing path, thereby enhancing global search capability. Experiments on the CEC2022 test functions demonstrate improved optimization performance of the algorithm. Furthermore, the algorithm is applied to the optimization design of off-grid HRES, integrating configurations such as photovoltaic panels, wind turbines, biomass generators and various battery types (Lead Acid battery/Lithium-Ion/Nickel-Iron), with lifecycle cost as the objective function while assessing energy costs. The results indicate that the nickel-iron battery system optimized by the improved DBO algorithm achieves the lowest lifecycle cost ($961,139) and energy cost ($0.3607/kWh), requiring a total of 1,329 PV panels, no wind turbines, and 268 nickel-iron battery units.

Feasibility Assessment of Using Wavestar Energy Converter in a Grid-Connected Hybrid Renewable Energy System (a case study)

Renewable energies, as a sustainable alternative to fossil fuels, confront a twofold challenge characterized by intermittency and the imperative to enhance reliability. Hybrid energy systems (HES) emerge as a pragmatic solution with the potential to mitigate carbon emissions and foster self-sufficiency within local communities. This investigation primarily seeks to ascertain the optimal configuration of a HES integrated with Wavestar wave energy converter, considering economic, technical, and environmental factors, tailored to meet the electricity demands of two cities in Iran including Chabahar and Anzali alongside of the Caspian sea and Oman sea, respectively. For this purpose, HOMER software is used for modeling and optimization the energy systems. In both locations, the optimal system includes photovoltaic (PV), wind turbine (WT), Wavestar wave energy converter (WEC), diesel generator (DG), and batteries which results in cost of energy (COE) of 0.136 and 0.109 in Chabahar and Anzali, respectively. Sensitivity analysis reveals that wind speed significantly impacts COE and reliability, also grid electricity purchases play a vital role. Economic uncertainty highlights varying importance between capital costs for PV and WT in Anzali and Chabahar. Furthermore, this study delves into the limitations posed by the fuel dependency of diesel generators. Finally, by conducting a thorough assessment of solar energy potential by GIS software, the research identifies a favorable location for the establishment of a solar power plant, contributing to the overall feasibility of the proposed hybrid energy systems.

Stability Improvement of Hybrid Renewable Energy Systems by Using Virtual Inertia Controller Based on Optimized FOPID with Harris Hawk Optimization

Stability Improvement of Hybrid Renewable Energy Systems by Using Virtual Inertia Controller Based on Optimized FOPID with Harris Hawk Optimization

Design analysis of a sustainable techno-economic hybrid renewable energy system: Application of solar and wind in Sigulu Island, Uganda

Sustainable energy is central to achieving the global Sustainable Development Goals (SDGs), particularly in electrifying underserved communities. This study examines Sigulu Island, which lacks grid electricity and relies on costly, polluting diesel generators. On-site assessments revealed a daily load demand of 1,455.705 kWh. This study designed and analyzed a Sustainable Techno-economic Hybrid Renewable Energy System (STHRES) combining solar photovoltaics and wind turbines, with battery backup, to meet the island's energy needs. The research adopted both qualitative and quantitative methods, gathering atmospheric weather conditions specific to Sigulu Island. Solar panels and wind turbines were identified as the most viable options, with the system incorporating 677 units of 1 kW solar panels and 27 units of 1 kW wind turbines, generating 839.97 kW and 640.08 kW daily, respectively. Additionally, 527 Li-ion batteries were used to store approximately 1480.05 kW of surplus energy demand to manage fluctuations capable of sustaining the island for 8 hours during a total blackout. The initial installation costs are estimated at $90,393.04 for solar PV, $27,729.82 for wind turbines, $159,169.81 for batteries, and $92,407.00 for the inverter. The STHRES is projected to save $56,917.93 annually, covering 15.4 % of the installation costs compared to diesel operations. Moreover, this system will reduce Uganda's carbon footprint by 436,035.6 kgCO₂ annually, equivalent to a 0.01 % reduction in national emissions. The proposed system decreases the Net Present Cost (NPC) from $426,617.60 to $369,699.67 and the Cost of Energy (COE) from $32.12/kWh to $ 27.79/kWh. With a 9 % Internal Rate of Return (IRR) and a 3 % Return on Investment (ROI), STHRES has a payback period of 8.2 years, demonstrating its financial and environmental benefits for Sigulu Island.

Analyze the Potential of Hybrid Renewable Energy Systems (HRES) for EV Charging Stations Across Four Provinces in Indonesia: Conduct Energy and Economic Evaluations

Analyze the Potential of Hybrid Renewable Energy Systems (HRES) for EV Charging Stations Across Four Provinces in Indonesia: Conduct Energy and Economic Evaluations

Modeling of hybrid renewable resources and comprehensive feasibility analysis of grid interactive energy system for commercial building: a case study

ABSTRACT The growing energy demand in commercial buildings and businesses poses significant environmental challenges due to the dependence on fossil fuel-based energy. To address this issue, increasing the use of renewable energy sources and decreasing reliance on fossil fuels is crucial. Commercial buildings, in particular, have many opportunities to harness energy from renewable resources, such as solar, wind, and biomass. This study analyzed historical energy consumption data from a commercial building using MATLAB to forecast future energy demands. A range of machine learning algorithms were employed to accurately predict energy consumption, including Ensemble Boosting, Ensemble Bagging, Decision Trees, Support Vector Machines (SVM), and Neural Networks (NN). A Hybrid Renewable Energy (HRE) system was designed to meet the building’s energy needs, integrating renewable sources with grid-interactive inverters. Performance metrics for the forecasting models were calculated and documented, followed by a detailed techno-economic and environmental feasibility analysis of the HRE system. Different levels of renewable energy penetration were explored to optimize HRE designs using load-following dispatch algorithms tailored to the commercial building’s specific needs. The study revealed that achieving a renewable energy proportion of 12.6% resulted in the lowest cost of electricity (COE) at $0.135 and a Net Present Cost (NPC) of $8.84 million. Additionally, a reliability study using load flow techniques confirmed the stability of the optimized Hybrid Renewable Energy system. The energy costs for varying levels of renewable penetration, ranging from 10% to 40% is also explored in this research. In summary, transitioning to renewable energy sources in commercial buildings can reduce environmental impact while ensuring a sustainable energy supply.

Design of reliable standalone utility-scale pumped hydroelectric storage powered by PV/Wind hybrid renewable system

With a target of 35 % renewable energy in the country’s energy mix by 2030, the goal of this research is to support the Libyan government’s plan to maximize the use of environmentally friendly and sustainable energy sources. This will be accomplished by making effective use of the region’s plentiful natural resources. The feasibility of wind and solar energy has been established by local research, and the presence of highlands that can store pumped hydropower (PHS) makes hybrid renewable energy systems (HRES) even more feasible. These systems might offer a sizable edge over competitors in the regional energy market. In this study, PHS was utilized to stabilize electricity production in addition to storing energy. A hydropower turbine provides electricity to the load, and the PHS system is powered by a PV/wind supply. By lowering the volatility of wind and photovoltaic power generation, the suggested system could offer a more dependable renewable energy source without requiring complicated control mechanisms. This strategy could encourage a wider adoption of renewable energy, which could be especially advantageous for developing nations. Under particular load and climate conditions, the energy production of different solar PV array and wind turbine farm configurations was estimated using the System Advisor Model (SAM) software. The ideal HRES configuration consists of a 290 MW wind farm, a 154 MW solar PV field, and a 508 MW PHS system. This configuration will provide 100 % of the annual electrical demand of 513 GWh, plus a 20 % safety margin. The levelized cost of energy (LCOE), estimated at $211.65/MWh, is reduced by this configuration. It is anticipated that the $929.02 million initial investment will be recovered in 11 years, and the system will turn a profit in the following 9 years. Also, the planned HRES will stop 532.17 tons of CO2 emissions from being released each year.

Optimizing a hybrid wind-solar-biomass system with battery and hydrogen storage using generic algorithm-particle swarm optimization for performance assessment

This paper investigates the optimal design of a hybrid renewable energy system, integrating wind turbines, solar photovoltaic systems, biomass, and battery and hydrogen storage to ensure a reliable energy supply at the lowest annual cost for a residential load in Kern County, USA. The hybrid generic algorithm particle swarm optimization (GAPSO) algorithm was adopted to determine the optimal configuration of parameters and cost-effectiveness, considering technical, economic, environmental, and social performance indicators. The generic algorithm (GA) and particle swarm optimization (PSO) validate the effectiveness of the proposed technique, showcasing its efficiency in system optimization. The findings indicate that GAPSO outperforms GA and PSO due to its rapid convergence, lowest final fitness value, and stable optimization process. The hybrid GAPSO's performance, combined with the different capacities of wind turbines (4,561 kW), solar PV (8,480 kW), biomass (2,261 kW), battery banks (8,000 kWh), and fuel cells (2,392 kW), resulted in an annual cost of $6,239,193; energy cost and net present value of $0.48/kWh and $101,333,937. The system maintained a supply loss of 0.8 %, achieved an availability index of 99.2 %, a renewable energy fraction of 88.87 %, GHGs emission of 953,615 kg, land use of 3,842,875 m2, and water consumption 528,678 L respectively. GAPSO achieved a 2.17 % and 0.01 % improvement in cost-effectiveness and 11.11 % increase in reliability compared to GA and PSO.