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International Journal of Renewable Energy Development
Published by Center of Biomass and Renewable Energy
ISSN : 22524940     EISSN : 27164519     DOI : https://doi.org/10.61435/ijred.xxx.xxx
Core Subject : Science, Engineering,
Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering
The International Journal of Renewable Energy Development - (Int. J. Renew. Energy Dev.; p-ISSN: 2252-4940; e-ISSN:2716-4519) is an open access and peer-reviewed journal co-published by Center of Biomass and Renewable Energy (CBIORE) that aims to promote renewable energy researches and developments, and it provides a link between scientists, engineers, economist, societies and other practitioners. International Journal of Renewable Energy Development is currently being indexed in Scopus database and has a listing and ranking in the SJR (SCImago Journal and Country Rank), ESCI (Clarivate Analytics), CNKI Scholar as well as accredited in SINTA 1 (First grade category journal) by The Directorate General of Higher Education, The Ministry of Education, Culture, Research and Technology, The Republic of Indonesia under a decree No 200/M/KPT/2020. The scope of journal encompasses: Photovoltaic technology, Solar thermal applications, Biomass and Bioenergy, Wind energy technology, Material science and technology, Low energy architecture, Geothermal energy, Wave and tidal energy, Hydro power, Hydrogen production technology, Energy policy, Socio-economic on energy, Energy efficiency, planning and management, Life cycle assessment. The journal also welcomes papers on other related topics provided that such topics are within the context of the broader multi-disciplinary scope of developments of renewable energy.
Arjuna Subject : Energi (semua kategori) - Energi Terbarukan, Keberlanjutan dan Lingkungan
Articles 13 Documents
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Search results for , issue "Vol 14, No 6 (2025): November 2025" : 13 Documents clear
Free hydrogen-deoxygenation of waste cooking oil into green diesel over Ni-Marble waste catalyst: Optimization and economic analysis Anggoro, Didi Dwi; Prasetyoko, Didik; Hartati, Hartati; Zakaria, Zaki Yamani; Le Monde, Brilliant Umara; Nurdiani, Maulida
International Journal of Renewable Energy Development Vol 14, No 6 (2025): November 2025
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2025.61241

Abstract

Diversifying energy through alternative sources, such as biofuels, is a practical and accessible option in Indonesia. This study aimed to optimize the yield of biofuel (green diesel) using Ni/marble waste as a catalyst. Deoxygenation offers a promising route for converting waste cooking oil (WCO) into valuable products. A Box–Behnken Design (BBD) was applied to assess the effects of key variables on the deoxygenation process using Response Surface Methodology (RSM). The variables included reaction time (2–6 h), reaction temperature (360–380 °C), and catalyst weight (1–3% w/w), with conversion percentage as the response. The results showed that reaction time and catalyst weight significantly influenced WCO deoxygenation (p < 0.05). The optimum conditions for maximum conversion were a reaction temperature of 373.64 °C, a catalyst weight of 3.45% w/w, and a reaction time of 4.35 h. Under these conditions, hydrocarbon selectivity reached 92.26%. Paraffins were the dominant fraction, confirming that the Ni/marble catalyst efficiently promoted deoxygenation with high selectivity toward C15–C18 hydrocarbons. These findings align with the proposed reaction mechanism, which involves decarboxylation, decarbonylation, and hydrodeoxygenation pathways. An economic evaluation under optimal conditions estimated a profit of $1.0469 per batch, demonstrating that converting waste cooking oil into green diesel is both technically feasible and economically attractive. Overall, integrating waste-derived catalysts with optimized deoxygenation technology provides a sustainable and profitable solution.
Modelling and analysis of wind loading effects for heliostat mirrors using computational fluid dynamics Ahmad, Naseer; Badar, Hafiz Waqas; Mughal, Khurram Hameed; Ali, Hafiz Umar; Waqas, Muhammad
International Journal of Renewable Energy Development Vol 14, No 6 (2025): November 2025
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2025.60977

Abstract

This study examines the impact of wind forces on the structural integrity of heliostat assemblies in concentrated solar power systems, specifically tailored to local climatic conditions. The objective is to assess how varying elevation angles influence aerodynamic parameters, thereby informing design optimizations for enhanced operational efficiency. A computational fluid dynamics approach, utilizing the standard k-ε turbulence model, second-order implicit time formulation, and the Green-Gauss cell-based method, was employed to simulate wind interactions with a heliostat model at elevation angles of 0°, 30°, 60°, and 90°. The simulation process encompassed model development, mesh refinement, boundary condition setup, and numerical solution techniques. Post-processing analysis focused on aerodynamic characteristics such as drag and lift forces, static and dynamic pressures, turbulent kinetic energy, and turbulence intensity. Results indicate that drag force increases with elevation angle, peaking at 90°, while lift force is maximized at 30°. Additionally, static and dynamic pressures, skin friction coefficients, and turbulence parameters exhibit strong dependence on the heliostat's elevation angle. The minimum values of the skin friction coefficient, drag coefficient, and turbulence intensity were found to be 0.0111, 0.3580, and 11.42%, respectively, at an elevation angle of 0°. Moreover, the finite element analysis of the heliostat structure to evaluate its resistance under wind loading demonstrated structural integrity with acceptable stress and displacement levels. These findings provide valuable insights for engineers and researchers aiming to optimize heliostat structural dimensions, thereby enhancing the economic and operational performance of concentrated solar power systems.
Data-driven reconstruction of solar spectrum in a class A+ LED solar simulator Wannakam, Khanittha; Boonmee, Chaiyant; Sukthang, Kreeta; Chudjuarjeen, Saichol; Romsai, Wattanawong; Watjanatepin, Napat
International Journal of Renewable Energy Development Vol 14, No 6 (2025): November 2025
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2025.61502

Abstract

High‑spectral‑fidelity solar simulators are indispensable for rigorous photovoltaic characterization, as they provide stable, reproducible irradiance that closely conforms to the AM 1.5G reference spectrum. The latest IEC 60904‑9:2020 standard imposes stringent limits on spectral mismatch (SM), coverage, and deviation, driving the need for innovative design strategies. This work introduces a data‑driven LED spectrum reconstruction methodology to engineer a Class A+ LED Solar Simulator (LSS) spectrum. Manufacturer‑provided spectral profiles spanning 300–1200 nm were digitized using a precision plot‑digitization tool and calibrated via a Spectral Mismatch Calculator to ensure wavelength alignment and intensity normalization. Custom numerical optimization algorithms then refined these datasets to compute the optimal mixing ratios of broadband phosphor‑converted white LEDs (400–900 nm), combined with targeted UV, visible, and NIR emitters. The finalized 13‑LED configuration achieved a Spectral Coverage (SPC) of 99.52% and a Spectral Deviation (SPD) of 17.42%, exceeding the Class A+ acceptance criteria while employing a minimal component count. Although minor uncertainties may originate from the digitization process, such as image resolution and axis calibration, these can be effectively mitigated by integrating direct numerical spectra supplied by manufacturers. This approach establishes an efficient, high‑accuracy framework for LSS spectral design. Future work will advance to hardware prototyping and empirical validation of the simulator’s irradiance spectrum under real‑world operating conditions, fully compliant with IEC 60904‑9:2020.
Predictive accuracy and characterisation of bio-oil yield from pyrolysis of Cocos nucifera: A comparison of traditional RSM and hybrid models Onokwai, Anthony O; Akuru, Udochukwu B.; Desai, Dawood A.
International Journal of Renewable Energy Development Vol 14, No 6 (2025): November 2025
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2025.61190

Abstract

The pressing demand for renewable energy has made biomass a quintessential alternative to fossil fuels. This study aims to develop and compare predictive models for optimising bio-oil yield from the intermediate pyrolysis of Cocos nucifera, utilising response surface methodology with the central composite design and hybrid models (PSO-ANFIS and GA-ANFIS). It seeks to characterize the bio-oil yield to investigate its quality for use as a biofuel. An experimental run was performed by varying pyrolysis operating parameters, namely, temperature (300–700°C), heating rate (6–30°C/min), residence time (5–25 minutes), particle size (0.5–4.5 mm), and nitrogen flow rate (10–50 mL/min).  Hybrid models (PSO-ANFIS and AN-FIS-GA) were used to predict the bio-oil yield to identify the most robust model. An optimum bio-oil yield (52.17 wt.%) was attained at a temperature, heating rate, residence time, particle size, and nitrogen flow rate of 510.2°C, 10.5°C/min, 5.2 minutes, 0.3 mm, and 17.3 mL/min, respectively.  The study shows that its hybrid models are scalable and outperform traditional techniques (RSM) in terms of predictive accuracy and computational efficiency. The GC-MS analysis identified over 200 compounds in bio-oil, comprising mainly phenols, esters, and oleic acids, which confirmed its suitability for producing biofuels, lubricants, and pharmaceuticals. Also, FTIR analysis confirms functional groups of biodiesel, adhesives, and resins. The PSO-ANFIS and GA-ANFIS models accurately predict the bio-oil yield, with the PSO-ANFIS model outperforming the other models with an R² of 0.994 and RMSE of 0.449 during the test phase, representing a two- to three-fold improvement over traditional RSM. Unlike conventional empirical models, the hybrid approach improves predictive accuracy and reduces the number of required experiments and computational errors, enabling real-time adjustments to the pyrolysis process, thereby advancing pyrolysis research and bio-oil optimization. This research is highly relevant for improving waste-to-energy production in regions where Cocos nucifera residues remain abundant, especially in emerging economies.
Techno-economic assessment and strategic proposal for designing and optimizing the required powered battery for an electric motorcycle under varying driving cycle tests Do, Tan-Thich; Dinh, Tan-Ngoc; Ly, Vinh-Dat
International Journal of Renewable Energy Development Vol 14, No 6 (2025): November 2025
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2025.61561

Abstract

Recently, many countries have committed to achieving net-zero emissions by 2050, making the adoption of electric motorcycles increasingly significant. The expansion of electric motorcycles has gained popularity due to their affordability, ease of use, and environmental benefits. In the design of electric motorcycles, optimizing energy efficiency and economic viability both technologically and economically is a key consideration. This study focuses on developing a mathematical model and strategic proposal with the step-by-step calculation for determining the required power battery for electric motorcycles under various driving cycle tests, implemented using Matlab software. The results analyze and discuss the effects of operating conditions on the electric motorcycle’s dynamic performance, average energy consumption, and battery cell and pack characteristics. Ultimately, the battery pack optimization strategy was proposed and conducted using the Mixed-Integer Linear Programming (MILP) approach. As a result, the Toshiba battery trademark was identified as the optimal choice for the required power battery in the electric motorcycle, considering both technological effectiveness and economic factors. The Toshiba battery pack has a capacity of 39 Ah, 17 cells, a mass of 13.94 kg, and a cost of $459, respectively. After designing and optimizing the required battery pack for the electric motorcycle, the model was validated to ensure that the pack’s energy exceeds the average energy consumption under varying driving cycle tests. Therefore, the model demonstrates high reliability. This study provides valuable insights into designing and evaluating the dynamic performance and battery pack characteristics of electric motorcycles.
Experimental investigation of inter-electrode distance and design in Cymbopogon citratus plant microbial fuel cells for sustainable energy production Attah, N'Gissa; Kongnine, Damgou Mani; Kpelou, Pali; Mouzou, Essowè
International Journal of Renewable Energy Development Vol 14, No 6 (2025): November 2025
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2025.61269

Abstract

Plant Microbial Fuel Cells (PMFCs) are bioelectrochemical systems that harness plant rhizodeposition to generate electricity. This technology enables electrical energy to be produced while the plant grows. However, the major problem preventing the commercialization of these cells is their low power. In the present study, a systematic investigation was conducted to ascertain the optimal configuration of these cells, with the objective of determining the optimum inter-electrode distance. In the present stidy, the lemongrass  plant (Cymbopogon citratus) was used as the main substrate source, plastic pots and graphite electrodes, while examining three single pair of electrodes configurations (PMFC-A, PMFC-B, PMFC-C), along with a unique configuration with three unaligned cathodes (PMFC-D) and three inter-electrode distances (5cm, 7.5cm and 12.5cm) were examined. The experiment focused on determining electrical parameters, plant mass growth rates and soil characteristics. These variables were measured before and after the experiment. The results indicated that the plant mass growth rate of PMFC-D exhibited the greatest magnitude (80.62%). The organic matter (OM) content in the soil exhibited an increase in each PMFC over the course of the experiment. PMFC-B exhibited the highest values of OM, electrical conductivity, and water content, respectively equal to 15.69%, 376.00µS/cm, and 15.46%. Conversely, it exhibited the lowest pH value (7.37). Electrical parameter measurements have demonstrated that PMFCs with a single pair of electrodes exhibit superior performance in comparison to those with three unaligned cathodes. Similarly, these measurements indicated that for the single pair electrode configuration, an inter-electrode distance of 7.5cm was optimal, yielding a maximum power density of 127mW/m².  The determination of the average internal resistance, open circuit voltage, and power density (PD), along with their standard deviations, demonstrated that PMFC-B exhibited superior performance. Furthermore, an analysis of its autonomy revealed that the PDmin it delivers, even in the absence of sunlight, is 16.90 mW/m². From these results, PMFC-B is the best configuration for lemongrass PMFC.
Optimization of sugarcane straw as a solid biofuel for thermochemical processes by water leaching pretreatment Assureira, Estela; Assureira, Marco
International Journal of Renewable Energy Development Vol 14, No 6 (2025): November 2025
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2025.61631

Abstract

Sugarcane straw, an abundant agricultural waste, has considerable potential as a renewable fuel due to its energy content, sustained generation, and CO2 neutrality but its direct utilization is limited by its high levels of ash, alkalis, S, Cl contents that cause severe slagging, fouling, and corrosion in boilers, as well as the harmful emissions released during combustion. To improve the fuel properties of sugarcane straw, a leaching pretreatment with distilled water was developed and applied to the residue under controlled conditions to evaluate the effects of water temperature, residence time and agitation of the leachate on the removal effectiveness of soluble ash-forming components. The leaching process was carried out in batches, maintaining a solid-to-liquid ratio of 1:30, and a feedstock size of 0.5–2 cm. Various combinations of temperature, residence time, and leachate agitation condition were tested to optimize the process. The optimal condition was established at 80 °C and 20 min with continuous agitation, which was applied to the residue, achieving reductions of 38.46% in ash, 78.26 in Cl, 57.14% in S, 9.09% in N, 54.61% in K2O, and 58.22% in Na2O, along with an increase in the high heating value, which reached 18.4 MJ/kg. These improvements reduce slagging, fouling and corrosion tendency, as indicated by lower predictive indices and higher ash fusion temperature reflected in the ternary phase diagram, and enhanced energy content. The improvements achieved make the washed sugarcane straw suitable for industrial biofuel applications, reducing issues associated with ash and emissions and providing higher energy content. The water leaching pretreatment also represents a valuable contribution since it can be easily replicated, and the upgraded residue has been valorized by being converted into a clean and sustainable fuel.
Design and control of a hybrid water pumping system using energy management for sustainable agricultural irrigation: A case study of the Sidi Bouzid region in Tunisia Amri, Akram; Moussa, Intissar; Khedher, Adel
International Journal of Renewable Energy Development Vol 14, No 6 (2025): November 2025
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2025.61626

Abstract

In this study, a renewable energy-powered Hybrid Water Pumping System (HWPS) is proposed for agricultural irrigation, designed to operate without reliance on battery storage. The system is adapted to the local climatic characteristics of the Sidi Bouzid region in Tunisia and is intended to regulate and coordinate water flow to effectively meet crop irrigation requirements. Hence, the system comprises three principal subsystems: A Wind Turbine (WT) driving a Doubly-Fed Induction Generator (DFIG) connected to the grid via rotor-side and grid-side converters; a Photovoltaic (PV) module integrated via a DC/DC boost converter; and a water pumping unit, consisting of an Induction Machine (IM) coupled to a centrifugal pump. The mathematical models of each subsystem were developed, and a control algorithms suite was implemented to enhance overall performance and energy efficiency. Maximum Power Point Tracking (MPPT) techniques were employed to optimize the energy harvested from renewable sources. A non-linear Sliding Mode Control (SMC) strategy was implemented to manage the DFIG power output, while Input-Output Feedback Linearization (IOFL) was applied to control the IM via a Voltage Source Inverter (VSI).Since the system operates without battery storage, a dynamic Energy Management System (EMS) is investigated to ensure optimal energy distribution, prioritizing solar energy during peak sunlight hours and transitioning to wind energy when solar availability declines. Simulation results validate the system’s effectiveness and demonstrate its potential for sustainable agricultural applications in rural areas. This approach offers a cost-effective and environmentally friendly sustainable solution for irrigation, contributing to improving water and energy security.
Thermal analysis of bifacial photovoltaic modules with single-axis trackers in a large power plant: Modeling by symbolic equations in tropical climates Vargas, Fabian Alonso Lara; Vargas Salgado, Carlos; Encalada, Alejandro Chacon; Alvarez, Jose Campos; Oviedo, Edison Ortega
International Journal of Renewable Energy Development Vol 14, No 6 (2025): November 2025
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2025.61489

Abstract

The thermal behavior of the single-axis tracked bifacial photovoltaic (PV) module is important for efficient energy extraction in large-scale power plants, especially in tropical regions under high irradiation and high ambient temperature. However, it is difficult to accurately predict their operating temperature due to the complex interaction between environmental variables and the characteristics of solar tracking. The available models, ranging from empirical correlations and computational fluid dynamics (CFD) simulations to machine learning methods, face challenges in terms of accuracy, interpretability, and computational load. This gap is addressed in this study, with the development of a modeling methodology based on symbolic regression (SR) utilizing genetic algorithms (GA) towards obtaining an explicit, interpretable Equation for the prediction of the PV module temperature in single-axis tracking systems. One year of data was collected at 5-minute intervals from a 19.9 MW PV plant located in San Marcos, Colombia, consisting of measurements for solar radiation, ambient temperature, wind speed, and module temperature. The constructed SR GA model achieved satisfactory prediction accuracy compared to classic models with the best root mean square error (RMSE = 4.14 °C) and R² (0.91) on the test data set. These results compare favorably with results from MLR (RMSE = 4.31 °C, R² = 0.90), the standard industry NOCT model (RMSE = 8.59 °C, R² = 0.60), and the empirical Skoplaki I model (RMSE = 5.92 °C, R² = 0.81). The resulting symbolic equation directly characterizes the effects of nonlinear solar radiation, ambient temperature, and wind speed, providing greater physical insight into the thermal dynamics of the system. An important finding is that the maximum temperature of the bifacial module is reached around 14:00h, probably due to the accumulation of temperature caused by solar tracking, which contrasts with what occurs in fixed-tilt monofacial technology. This study demonstrates that the symbolic regression technique with a genetic algorithm kernel can produce accurate, interpretable, and computationally economical models for advanced photovoltaic systems.
Multi-objective HVAC control using genetic programming for grid-responsive commercial buildings Waheed, Sibtain; Li, Shuhong
International Journal of Renewable Energy Development Vol 14, No 6 (2025): November 2025
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2025.61410

Abstract

Commercial buildings are significant energy consumers, with their heating, ventilation, and air conditioning (HVAC) systems being major contributors. Optimizing these systems is crucial for energy conservation, yet advanced artificial intelligence methods like Deep Reinforcement Learning (DRL) often produce opaque black-box solutions. While post-hoc explanation methods can offer some insight, they are often inexact and fail to render the core decision logic fully transparent, hindering trust and practical implementation. This paper presents a novel approach using Genetic Programming (GP) to automatically design HVAC control strategies that are both highly effective and inherently understandable. The novelty of our framework lies in its direct evolution of interpretable, multi-objective control policies that holistically co-optimize energy efficiency, occupant thermal comfort, and integrated Demand Response (DR) for a complex multi-zone system a combination not extensively explored in prior GP-HVAC research. We applied this framework to manage the central air handling unit of a simulated multi-zone office building, enabling it to dynamically adjust key settings like air temperature and fan pressure. Rigorous testing in a validated EnergyPlus simulation environment showed that the GP-designed control policies reduced annual HVAC energy use by 40.9% compared to standard ASHRAE A2006 guidelines, 28.4% against the advanced ASHRAE G36 standard, and a notable 9.3% more than a state-of-the-art DRL controller. These substantial energy savings were achieved while maintaining excellent occupant thermal comfort for 98.8% of occupied hours. Furthermore, the GP controller demonstrated robust performance during Demand Response scenarios, achieving a 72.1% reduction in peak power draw. A key outcome is that these high-performing strategies are expressed in a transparent format allowing direct inspection and understanding. This research establishes Genetic Programming as a compelling method for creating intelligent HVAC controls that are not only efficient and grid-responsive but also transparent, fostering greater confidence in advanced building automation.

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