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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 709 Documents
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Energy optimization management of microgrid using improved soft actor-critic algorithm Yu, Zhiwen; Zheng, Wenjie; Zeng, Kaiwen; Zhao, Ruifeng; Zhang, Yanxu; Zeng, Mengdi
International Journal of Renewable Energy Development Vol 13, No 2 (2024): March 2024
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2024.59988

Abstract

To tackle the challenges associated with variability and uncertainty in distributed power generation, as well as the complexities of solving high-dimensional energy management mathematical models in mi-crogrid energy optimization, a microgrid energy optimization management method is proposed based on an improved soft actor-critic algorithm. In the proposed method, the improved soft actor-critic algorithm employs an entropy-based objective function to encourage target exploration without assigning signifi-cantly higher probabilities to any part of the action space, which can simplify the analysis process of distributed power generation variability and uncertainty while effectively mitigating the convergence fragility issues in solving the high-dimensional mathematical model of microgrid energy management. The effectiveness of the proposed method is validated through a case study analysis of microgrid energy op-timization management. The results revealed an increase of 51.20%, 52.38%, 13.43%, 16.50%, 58.26%, and 36.33% in the total profits of a microgrid compared with the Deep Q-network algorithm, the state-action-reward-state-action algorithm, the proximal policy optimization algorithm, the ant-colony based algorithm, a microgrid energy optimization management strategy based on the genetic algorithm and the fuzzy inference system, and the theoretical retailer stragety, respectively. Additionally, com-pared with other methods and strategies, the proposed method can learn more optimal microgrid energy management behaviors and anticipate fluctuations in electricity prices and demand.
Evaluating the performance of the Anwaralardh photovoltaic power generation plant in Jordan: Comparative analysis using artificial neural networks and multiple linear regression modeling Alma'asfa, Suhaib Ibrahim; Fraige, Feras Younes; Abdul Aziz, Mohd Sharizal; Khor, Chu Yee; Al-Khatib, Laila A
International Journal of Renewable Energy Development Vol 13, No 4 (2024): July 2024
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2024.60156

Abstract

The global energy demand is rising, driven by population growth, economic development, and industrialization. Shifting towards renewable energy, like solar energy, is gaining momentum worldwide because of ecological concerns and resource depletion. This paper aims to utilize Artificial Neural Networks (ANNs) and multiple linear regression (MLR) modeling techniques to evaluate the productivity of 11 MW photovoltaic (PV) solar power plant currently operational in Jordan. The case study reveals that both models can be used to predict the daily, monthly, and yearly average power produced and system efficiency with reasonable accuracy. The ANN model exhibited promising results, where the best value for the coefficient of determination (R2) and mean absolute percentage error (MAPE) for training were 95.85% and 0.59%, respectively. However, R2 was 93.7%, and MAPE was 1.27% for validation tests. All these results were achieved using a 7-6-1 model, with a sample ratio of 1:1 for the data allocated in training and validation. When using multiple linear regression, the R2 and standard error values were 93.42% and 0.17%. On the other hand, the results showed that the yearly output power for actual and predicted by both models over the year was 24,399 MWh, 24,538 MWh, and 24,401 MWh, respectively. This research showed valuable results in the monthly output power for solar cells at the Anwaralardh PV power system project, contributing to a better understanding of solar energy generation in arid desert climates and emphasizing the potential of solar power plants to play a crucial role in achieving SDG 7 objectives.
Comparison of lithium sources on the electrochemical performance of LiNi0.5Mn1.5O4 cathode materials for lithium-ion batteries Sudaryanto, Sudaryanto; Salsabila, Nadhifah; Sari, Puspita Ayu Kusuma; Fachrudin, Adinandra Caesar; Salsabila, Adinda Atalya; Nursanto, Eduardus Budi; Priyono, Slamet; Jodi, Heri; Gumelar, Muhammad Dikdik
International Journal of Renewable Energy Development Vol 13, No 3 (2024): May 2024
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2024.59662

Abstract

In order to fulfill the demand for high energy and capacity, an electrode with high-voltage capability, namely LiNi0.5Mn1.5O4 (LNMO) that has an operating potential of up to 4.7 V vs Li/Li+, is currently becoming popular in Li-ion battery chemistries. This research produced LNMO by using a solid-state method with only one-step synthesis route to compare its electrochemical performance with different lithium sources, including hydroxide (LNMO-LiOH), acetate (LNMO-LiAce), and carbonate (LNMO-LiCar) precursors. TGA/DSC was first performed for all three sample precursors to ensure the optimal calcination temperature, while XRD and SEM characterized the physical properties. The electrochemical measurements, including cyclic voltammetry and charge-discharge, were conducted in the half-cell configurations of LNMO//Li-metal using a standard 1 M LiPF6 electrolyte. LNMO-LiOH samples exhibited the highest purity and the smallest particle size, with values of 93.3% and 418 nm, respectively. In contrast, samples with higher impurities, such as LNMO-LiCar, mainly in the form of LixNi1-xO (LiNiO), displayed the largest particle size. The highest working voltage possessed by LNMO-LiOH samples was 4.735 V vs Li/Li+. The results showed that LNMO samples with LiNiO impurities would affect the reaction behavior that occurs at the cathode-electrolyte interface during the release of lithium-ions, resulting in high resistance at the battery operations and decreasing the specific capacity of the LNMO during discharging. The highest value, shown by LNMO-LiOH, was up to 92.75 mAh/g. On the other side, LNMO-LiCar only possessed a specific capacity of 44.57 mAh/g, indicating a significant impact of different lithium sources in the overall performances of LNMO cathode.
Automatic control of constant temperature and humidity in building air conditioning systems based on frequency domain analysis Wang, Lihui
International Journal of Renewable Energy Development Vol 13, No 4 (2024): July 2024
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2024.60102

Abstract

How to solve their automatic control of constant temperature and humidity gradually becomes a research hotspot as the continuous upgrading of air conditioning systems. This study aims to optimize the traditional proportional-integral-differential controller for improvement to solve the time-delay instability phenomenon in temperature and humidity control. The objective of this study is to optimize existing proportional-integral-differential controllers to improve the time-delay instability problem that is common in temperature and humidity control. Firstly, it treats the controlled object as a first-order and second-order system with time-delay characteristics. Next, the Smith predictor controller is generalized equivalent to ensure that the equivalent system does not contain time-delay. Finally, an analysis of the first-order and second-order closed-loop control system is conducted by combining Smith predictive controller and proportional-integral-differential controller. The system achieves the goal of automatic control of constant temperature and humidity by adjusting the control parameters. The experiment showcased that the temperature control time of the proposed control scheme under first-order and second-order time-delays was 16 s and 3 s, respectively. Meanwhile, the humidity control time was 14 s and 13 s, respectively. In practical applications, the proposed control scheme achieved good control effects in all four seasons. This indicates that the controller designed in this study possesses good control performance. It also can achieve the goal of constant temperature and humidity control. This can provide technical support for the automation control of air conditioning systems.
Production of biodiesel by using CaO nano-catalyst synthesis from mango leaves extraction Mahmood, Sarah Shakir; Al-Yaqoobi, Atheer Mohammed
International Journal of Renewable Energy Development Vol 13, No 6 (2024): November 2024
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2024.60469

Abstract

Development and population expansion have the lion's share of driving up the fuel cost. Biodiesel has considerable attention as a renewable, ecologically friendly and alternative fuel source. In this study, CaO nanocatalyst is produced from mango leaves as a catalysis for the transesterification of waste cooking oil (WCO) to biodiesel. The mango tree is a perennial plant, and its fruit holds significant economic worth due to its abundance of vitamins and minerals. This plant has a wide geographical range and its leaves can be utilized without any negative impact on its growth and yield. An analysis was conducted to determine the calcium content in the fallen leaves, revealing a significant quantity of calcium that holds potential for utilization. The catalyst was characterized by different analytic techniques such as XRD, SEM-EDS, FT-IR, and BET analyses. Several parameters impacted on the transesterification process were exploited by conventional transesterification (batch). The result revealed that the optimum reaction was reached at a methanol to oil ratio of 50% w/w, catalyst loading of 3%, temperature of 65℃ and reaction time of 1.5 h with a yield of 93.21%, and the activation energy of the transesterification reaction was found to be 38.906 KJ mol-1. The reaction was verified to be irreversible pseudo-first order based on a linear Arrhenius plot and a high R2 value. The catalyst shows good stability and catalytic activity when it is reused and the yield was found to be 80.293% in the 5th cycle.
Multi-objective decision optimization design for building energy-saving retrofitting design based on improved grasshopper optimization algorithm Bao, Xin; Zhang, Jinghui
International Journal of Renewable Energy Development Vol 13, No 6 (2024): November 2024
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2024.60483

Abstract

With the national emphasis on building energy efficiency planning, energy efficiency optimization in existing buildings requires renovation measures based on multi-objective factors. In order to get the optimal solution in the multi-objective decision-making of renovation, the study proposes a class of improved grasshopper optimization algorithms. The process employs a systematic methodology to identify an optimal energy renovation method, taking into account the specific characteristics of the building environment. It then classifies and formulates the energy reduction substitution items for building renovation, and finally, it synchronizes the cost of the renovation project as a measure for decision-making. The elite inverse strategy approach enhances the grasshopper optimization algorithm to facilitate the multi-objective decision-making process associated with building renovation measures. The results showed that the improved grasshopper optimization algorithm could achieve a decision accuracy of 98.8% for the test samples, which was 5.5% higher than the accuracy of the particle swarm optimization algorithm. Repeated run tests of the research algorithm for multi-objective decision making yielded a mean decision fitness value of 2.34×104 and a data extreme value of 0.38×104. Compared to other algorithms improved grasshopper optimization algorithm converged in a lower range of fitness values, which indicated that the algorithm worked well for multi-objective optimization and the model repeatability was good. The research algorithm was used to decide the energy efficient renovation planning of the building and the power consumption of the renovated power supply system was reduced by 23.7%-49.6%. This indicates that the renovated building has better energy efficiency and can provide a reliable technical direction for decision-making optimization of building energy efficiency renovation.
A techno-economic and environmental analysis of co-firing implementation using coal and wood bark blend at circulating fluidized bed boiler Cahyo, Nur; Sulistyowati, Desy; Rahmanta, Mujammil Asdhiyoga; Felani, Muhamad Iqbal; Soleh, Mochamad; Paryanto, Paryanto; Prismantoko, Adi; Hariana, Hariana
International Journal of Renewable Energy Development Vol 13, No 4 (2024): July 2024
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2024.60234

Abstract

The study aimed to explore the effects of biomass co-firing of coal using acacia wood bark at circulating fluidized bed (CFB) boiler coal-fired power plant with 110 MWe capacity. The analysis focused on main equipment parameters, including the potential for slagging, fouling, corrosion, agglomeration, fuel cost, and specific environmental factors. Initially, coal and acacia wood bark fuel were blended at a 3% mass ratio, with calorific values of 8.59 MJ/kg and 16.59 MJ/kg, respectively. The corrosion due to chlorine and slagging potential when using wood bark was grouped into the minor and medium categories. The results showed that co-firing at approximately 3% mass ratio contributed to changes in the upper furnace temperature due to the variation in heating value, high total humidity, and a less homogeneous particle size distribution. Significant differences were also observed in the temperature of the lower furnace area, showing the presence of a foreign object covering the nozzle, which disturbed the ignition process. A comparison of the seal pot temperature showed imbalances as observed from the temperature indicators installed on both sides of boiler, with specific fuel consumption (SFC) increasing by approximately 0.17%. During the performance test, the price of acacia wood bark was 0.034 USD/kg, resulting in fuel cost of 0.023355 USD/kWh, adding 0.061 cent/kWh to coal firing cost. Despite co-firing, the byproducts of the combustion process, such as SO2 and NOx, still met environmental quality standards in accordance with government regulations. However, a comprehensive medium- and long-term impact evaluation study should be carried out to implement co-firing operations using acacia wood bark at coal-fired power plant. Based on the characteristics, such as low calorific value, with high ash, total moisture, and alkali, acacia wood bark showed an increased potential to cause slagging and fouling.
The role of economic complexity in shaping the energy-growth nexus: Evidence from cross-country panel data Al-Silefanee, Rebean
International Journal of Renewable Energy Development Vol 13, No 2 (2024): March 2024
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2024.60055

Abstract

The study investigated the interplay between energy consumption (EN), economic growth (EG), and economic complexity across 59 countries from 2000 to 2018. Employing panel data methods, the research examined various models to estimate long-term effects while addressing unobserved heterogeneity and potential biases. Results indicate significant relationships between EG, EN, and economic complexity. Notably, the economic complexity index (ECI) displayed a positive effect on economic development, while trade openness and foreign direct investment showed varying impacts. The study identified a positive association between EG and EN, suggesting that increased energy consumption accompanies economic growth. However, a higher capital-to-labor ratio was associated with lower EN, indicating a substitution effect. Of particular note is the significant positive impact of the interaction between ECI and EN on GDP across various models. In the Country Fixed Effects Model, a one-unit increase in the interaction correlated with a 0.026 unit increase in GDP (p < 0.001). Similarly, significant positive relationships were observed in the Panel EGLS and FMOLS models, with coefficients of 0.055 and 0.031, respectively (p < 0.001 and p = 0.011). Conversely, all models consistently demonstrated a negative relationship between economic complexity and GDP, with coefficients ranging from -0.062 to -0.089 (p < 0.001). These findings underscore the importance of considering economic complexity and energy consumption in policy interventions aimed at promoting sustainable economic growth. Policymakers are encouraged to adopt comprehensive approaches that account for the complex interplay of various factors influencing economic development and energy consumption to formulate effective strategies.
Unveiling the Nexus: Analyzing foreign direct investment and energy consumption in shaping carbon footprints across Africa’s leading CO2-emitting countries Njie, Yahya; Wang, Weidong; Liu, Lin; Abdullah, .
International Journal of Renewable Energy Development Vol 13, No 5 (2024): September 2024
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2024.60315

Abstract

Carbon emissions have become a pressing global concern because of its contribution to climate change and environmental degradation. Given the urgency to tackle climate change, especially by reducing carbon emissions, this study focuses on Africa’s leading CO2 emitters from 2000 to 2020. The aims of the study are; to examine whether there is evidence of an energy-Kuznets Curve among the leading CO2 emitters in Africa, to examine whether there is evidence of an FDI-Kuznets Curve among the leading CO2 emitters in Africa, and to Identify the turning points. The study employs an innovative analysis of unbalanced panel data utilizing sophisticated econometric techniques, the contemporaneous correlation methodology, which are; the feasible generalized least squares (FGLS), and the panel-corrected standard errors (PCSE) to uncover insights. The results reveal consistency across all employed techniques. The study confirms the existence of an Energy-Kuznets Curve among the leading CO2 emitters in Africa; it also finds evidence of a U-shaped relationship between foreign direct investment and carbon emissions among the leading CO2 emitters in Africa; finally, it also identifies crucial turning points at 2760.12kg and 2886.29kg of oil equivalent per capita for energy use and 6.89% and 6.17% for FDI inflow, respectively. By investigating the factors influencing carbon emissions and evaluating their impacts, our study offers valuable insights for policymakers. These findings can inform the development of targeted interventions to curb emissions intensity, enhance energy efficiency, and foster the adoption of renewable energy sources.
Energy transition and sustainable development in Malaysia: Steering towards a greener future Ghosn, Fadi; Zreik, Mohamad; Awad, Ghina; Karouni, George
International Journal of Renewable Energy Development Vol 13, No 3 (2024): May 2024
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2024.60110

Abstract

In the evolving landscape of global energy dynamics, Malaysia stands as a pivotal example of a nation actively transitioning towards renewable energy and sustainable development. This paper provides a comprehensive analysis of Malaysia's energy sector transformation, underpinned by the government's commitment to reducing carbon emissions and mitigating the impacts of climate change. The objective of this research is to delve into the intricacies, opportunities, and challenges of steering Malaysia towards a greener future, with a particular focus on the shift from reliance on fossil fuels to the adoption of renewable energy sources such as solar, wind, and biomass. Employing a mixed-method approach, this study synthesizes existing literature, policy documents, and case studies to examine the current state and historical context of energy use in Malaysia, analyze government initiatives and policy frameworks, explore technological advancements, and assess the environmental and socioeconomic impacts of the energy transition. Results indicate that despite facing challenges such as financial investment, technological advancement, and public acceptance, collaborative efforts between the government, private sector, and communities have led to significant progress in promoting renewable energy. The paper concludes that Malaysia's energy transition represents a critical step towards achieving a balance between economic growth and environmental preservation, setting a precedent for sustainable development in the Southeast Asian region. This transition is not only essential for climate change mitigation but also presents opportunities for economic diversification, energy security, and social inclusivity. The study ultimately calls for continued innovation, supportive policies, and international cooperation to overcome remaining barriers and fully realize the potential of renewable energy in Malaysia.

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