Pub Date : 2025-10-30DOI: 10.1016/j.cles.2025.100212
Ribwar Abdulrahman , Haval Kukha Hawez , Rawezh Mustafa
Biodiesel may be considered a renewable and clean energy source that can contribute to reducing global greenhouse emissions and global warming phenomena. Biodiesel possesses several advantages over traditional petroleum diesel fuel, such as fewer greenhouse gas emissions and environmentally friendly fuel. The local olive oil factories dispose of the olive pomace, a non-edible by-product stream from the production process, with low production costs. Olive oil and pomace oil can be considered appropriate feedstock supporting biodiesel production worldwide. In this study, biodiesel was produced from a local olive oil sample sourced from Harmota olive oil in the Koya district of Iraqi Kurdistan. The produced biodiesel was also examined by several laboratory tests, such as density and cetane value, and the results were compared well with ASTM D6751 standards. The transesterification process utilized potassium hydroxide (KOH) as a catalyst, with varying methanol-to-oil molar ratios. The optimal conditions were identified as a 7:1 methanol-to-oil ratio and 0.5 grams of KOH, achieving a high biodiesel yield of approximately 91%. The resulting biodiesel demonstrated key fuel properties—density (879 kg/m³), viscosity (5.125 mm²/s), cetane number (64), and flash point (165 °C)—which are all within the ASTM D6751 biodiesel standard limits. Furthermore, this study shows the intriguing possibilities of using Harmota olive oil and its by-product, olive pomace oil, as a sustainable and effective feedstock. But it goes beyond that: guaranteeing high-quality biodiesel that is both affordable and environmentally benign depends on process optimization.
{"title":"The utilization of Harmota olive oil to produce a sustainable biofuel","authors":"Ribwar Abdulrahman , Haval Kukha Hawez , Rawezh Mustafa","doi":"10.1016/j.cles.2025.100212","DOIUrl":"10.1016/j.cles.2025.100212","url":null,"abstract":"<div><div>Biodiesel may be considered a renewable and clean energy source that can contribute to reducing global greenhouse emissions and global warming phenomena. Biodiesel possesses several advantages over traditional petroleum diesel fuel, such as fewer greenhouse gas emissions and environmentally friendly fuel. The local olive oil factories dispose of the olive pomace, a non-edible by-product stream from the production process, with low production costs. Olive oil and pomace oil can be considered appropriate feedstock supporting biodiesel production worldwide. In this study, biodiesel was produced from a local olive oil sample sourced from Harmota olive oil in the Koya district of Iraqi Kurdistan. The produced biodiesel was also examined by several laboratory tests, such as density and cetane value, and the results were compared well with ASTM D6751 standards. The transesterification process utilized potassium hydroxide (KOH) as a catalyst, with varying methanol-to-oil molar ratios. The optimal conditions were identified as a 7:1 methanol-to-oil ratio and 0.5 grams of KOH, achieving a high biodiesel yield of approximately 91%. The resulting biodiesel demonstrated key fuel properties—density (879 kg/m³), viscosity (5.125 mm²/s), cetane number (64), and flash point (165 °C)—which are all within the ASTM D6751 biodiesel standard limits. Furthermore, this study shows the intriguing possibilities of using Harmota olive oil and its by-product, olive pomace oil, as a sustainable and effective feedstock. But it goes beyond that: guaranteeing high-quality biodiesel that is both affordable and environmentally benign depends on process optimization.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"12 ","pages":"Article 100212"},"PeriodicalIF":0.0,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01DOI: 10.1016/j.cles.2025.100209
Hasanur Zaman Anonto , Md Ismail Hossain , Abu Shufian , Protik Parvez Sheikh , Sadman Shahriar Alam , Md. Shaoran Sayem , S M Tanvir Hassan Shovon
This study investigates energy consumption trends and environmental influences by analyzing time-series data to explore the correlation between temperature, humidity, renewable energy contributions, and energy demand. The research focuses on developing an advanced hybrid machine learning model using LightGBM, XGBoost, LSTM, and SHAP to enhance the accuracy and interpretability of energy consumption predictions. Using data from January 2022 to January 2025 across residential, commercial, and industrial buildings, the study examines the impact of temperature fluctuations, humidity, and renewable energy integration on energy consumption. Temperature dependency is further explored in the study, where it is shown that energy consumption is directly influenced by temperature, with energy use at 20 °C being 2000 kWh, increasing to 3200 kWh at 30 °C (on an annual basis), further confirming the shaped dependency with increased cooling demands during warmer months. Additionally, energy consumption varies significantly across building types, with industrial buildings showing higher and more stable energy demands than residential and commercial buildings. Results indicate that XGBoost provides the best predictive performance, with an RMSE of 118.24 and an R² score of 0.9871, followed by LSTM with an RMSE of 135.86 and an R² score of 0.9752, and Linear Regression with RMSE of 187.76 and an R² score of 0.9672. The hybrid model effectively predicts energy consumption and offers valuable insights into how environmental factors influence energy demands across different building types. The findings contribute to optimizing energy management strategies, improving innovative grid systems, and promoting sustainable building operations while highlighting the role of renewable energy in shaping energy consumption patterns.
{"title":"Analyzing energy consumption trends and environmental influences: A time-series study on temperature, renewables, and demand correlations","authors":"Hasanur Zaman Anonto , Md Ismail Hossain , Abu Shufian , Protik Parvez Sheikh , Sadman Shahriar Alam , Md. Shaoran Sayem , S M Tanvir Hassan Shovon","doi":"10.1016/j.cles.2025.100209","DOIUrl":"10.1016/j.cles.2025.100209","url":null,"abstract":"<div><div>This study investigates energy consumption trends and environmental influences by analyzing time-series data to explore the correlation between temperature, humidity, renewable energy contributions, and energy demand. The research focuses on developing an advanced hybrid machine learning model using <em>LightGBM, XGBoost</em>, LSTM, and SHAP to enhance the accuracy and interpretability of energy consumption predictions. Using data from January 2022 to January 2025 across residential, commercial, and industrial buildings, the study examines the impact of temperature fluctuations, humidity, and renewable energy integration on energy consumption. Temperature dependency is further explored in the study, where it is shown that energy consumption is directly influenced by temperature, with energy use at 20 °C being 2000 kWh, increasing to 3200 kWh at 30 °C (on an annual basis), further confirming the shaped dependency with increased cooling demands during warmer months. Additionally, energy consumption varies significantly across building types, with industrial buildings showing higher and more stable energy demands than residential and commercial buildings. Results indicate that <em>XGBoost</em> provides the best predictive performance, with an RMSE of 118.24 and an R² score of 0.9871, followed by LSTM with an RMSE of 135.86 and an R² score of 0.9752, and Linear Regression with RMSE of 187.76 and an R² score of 0.9672. The hybrid model effectively predicts energy consumption and offers valuable insights into how environmental factors influence energy demands across different building types. The findings contribute to optimizing energy management strategies, improving innovative grid systems, and promoting sustainable building operations while highlighting the role of renewable energy in shaping energy consumption patterns.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"12 ","pages":"Article 100209"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145010062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-29DOI: 10.1016/j.cles.2025.100211
J. Sturtevant, R.A. McManamay, A. Corry-Roberts, S. Nguyen
Energy production has many life cycles, each requiring expansive infrastructure and a significant spatial footprint in the landscape. As energy systems expand and technologies transition from non-renewable to renewable energy sources, it is imperative to accurately quantify the amount of land needed. Since life cycles of different technologies may require very different conversion of land surfaces, land transformation estimates can provide a standardized measure of the efficiency of an energy technology. Although there is an abundance of existing literature, spatial footprint estimates vary substantially among technologies and life-cycle stages. These varied sources could benefit from a standardized comparison and validation using a comprehensive and consistent ground-truth assessment. The National Water Energy Land Dataset (NWELD) provides comprehensive and spatially explicit mapping of land used for energy technology. Therefore, we present a methodological re-analysis of land used for energy by comparing spatially explicit observations from NWELD to coefficients found in literature for specific fuels and life cycles. Literature was compiled using a systematic methodology, filtered to collect pertinent data values, and summarized. NWELD land requirements were calculated and coupled with U.S. Energy Information Administration (EIA) data to determine the energy production per technology. Our results suggest that the total life cycle of NWELD’s natural gas, oil, nuclear, and coal have higher median land footprints than what is reported in literature, except for biomass. Furthermore, we find that literature resources recycle common data points, which if inaccurate, could lead to error propagation in estimating land used for energy.
{"title":"Re-analysis of land used for energy: Comparison of spatially explicit observations and literature sources","authors":"J. Sturtevant, R.A. McManamay, A. Corry-Roberts, S. Nguyen","doi":"10.1016/j.cles.2025.100211","DOIUrl":"10.1016/j.cles.2025.100211","url":null,"abstract":"<div><div>Energy production has many life cycles, each requiring expansive infrastructure and a significant spatial footprint in the landscape. As energy systems expand and technologies transition from non-renewable to renewable energy sources, it is imperative to accurately quantify the amount of land needed. Since life cycles of different technologies may require very different conversion of land surfaces, land transformation estimates can provide a standardized measure of the efficiency of an energy technology. Although there is an abundance of existing literature, spatial footprint estimates vary substantially among technologies and life-cycle stages. These varied sources could benefit from a standardized comparison and validation using a comprehensive and consistent ground-truth assessment. The National Water Energy Land Dataset (NWELD) provides comprehensive and spatially explicit mapping of land used for energy technology. Therefore, we present a methodological re-analysis of land used for energy by comparing spatially explicit observations from NWELD to coefficients found in literature for specific fuels and life cycles. Literature was compiled using a systematic methodology, filtered to collect pertinent data values, and summarized. NWELD land requirements were calculated and coupled with U.S. Energy Information Administration (EIA) data to determine the energy production per technology. Our results suggest that the total life cycle of NWELD’s natural gas, oil, nuclear, and coal have higher median land footprints than what is reported in literature, except for biomass. Furthermore, we find that literature resources recycle common data points, which if inaccurate, could lead to error propagation in estimating land used for energy.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"12 ","pages":"Article 100211"},"PeriodicalIF":0.0,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145003952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-27DOI: 10.1016/j.cles.2025.100210
Zuriani Mustaffa , Mohd Herwan Sulaiman
Wind power generation prediction is critical for the effective integration of renewable energy into the power grid, supporting stability, reliability, and sustainability in electricity supply. However, the inherent variability and non-linear characteristics of wind patterns present substantial challenges to accurate prediction. This study tackles these challenges by utilizing the Random Forest (RF) algorithm, an ensemble learning approach renowned for its ability to capture complex, non-linear relationships in data. The RF model’s performance is compared with three commonly used prediction techniques: Neural Networks (NN), Extreme Gradient Boosting (XGBoost), and Linear Regression (LR). The models were evaluated using historical wind power data and key meteorological variables, with performance assessed through multiple metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Maximum Error (MAX), Standard Deviation (STD DEV), and R-squared (R²). The results indicate that the RF model achieved the best performance, with an RMSE of 55.11 and an R² of 0.9882, outperforming the NN, XGBoost, and LR models. Specifically, the NN model achieved an RMSE of 95.5 with an R² of 0.9651, XGBoost had an RMSE of 93.32 and an R² of 0.9666, and the LR model exhibited an RMSE of 144.45 with an R² of 0.9084. These findings demonstrate RF's superior predictive accuracy and robustness, making it a powerful tool for wind power forecasting, providing valuable insights for grid management and renewable energy planning.
{"title":"Random forest based wind power prediction method for sustainable energy system","authors":"Zuriani Mustaffa , Mohd Herwan Sulaiman","doi":"10.1016/j.cles.2025.100210","DOIUrl":"10.1016/j.cles.2025.100210","url":null,"abstract":"<div><div>Wind power generation prediction is critical for the effective integration of renewable energy into the power grid, supporting stability, reliability, and sustainability in electricity supply. However, the inherent variability and non-linear characteristics of wind patterns present substantial challenges to accurate prediction. This study tackles these challenges by utilizing the Random Forest (RF) algorithm, an ensemble learning approach renowned for its ability to capture complex, non-linear relationships in data. The RF model’s performance is compared with three commonly used prediction techniques: Neural Networks (NN), Extreme Gradient Boosting (XGBoost), and Linear Regression (LR). The models were evaluated using historical wind power data and key meteorological variables, with performance assessed through multiple metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Maximum Error (MAX), Standard Deviation (STD DEV), and R-squared (R²). The results indicate that the RF model achieved the best performance, with an RMSE of 55.11 and an R² of 0.9882, outperforming the NN, XGBoost, and LR models. Specifically, the NN model achieved an RMSE of 95.5 with an R² of 0.9651, XGBoost had an RMSE of 93.32 and an R² of 0.9666, and the LR model exhibited an RMSE of 144.45 with an R² of 0.9084. These findings demonstrate RF's superior predictive accuracy and robustness, making it a powerful tool for wind power forecasting, providing valuable insights for grid management and renewable energy planning.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"12 ","pages":"Article 100210"},"PeriodicalIF":0.0,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144913657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-12DOI: 10.1016/j.cles.2025.100208
Lucas G. Pereira , Marília Ieda da S. Folegatti , Nilza Patrícia Ramos , Cristiano Alberto de Andrade , Anna Leticia M.T. Pighinelli , Rosana Galindo , Joaquim E.A. Seabra
Brazil’s National Biofuel Policy (RenovaBio) promotes negative emission technologies (NETs) as one of the instruments to reduce the carbon intensity (CI) of biofuels; however, no mill in the country has integrated such strategies into its production processes to date.
The present study examined the potential impact of two promising NETs (i.e, bioenergy with carbon capture and storage (BECCS) and field application of biochar) on the CI of sugarcane ethanol, calculated using the methodological approach adopted by RenovaBio. In addition, the assessment used data from the agricultural and industrial stages provided by 62 certified sugarcane mills.
Results show that these strategies have the potential to significantly reduce the CI of ethanol. The obtained value of +32.8 g CO2e/MJ of hydrated ethanol (without NETs considered) could be reduced to +15.9 (with the application of 1.0 t biochar/ha), +10.4 (with BECCS from fermentation), and −81.3 (with BECCS from combustion). Ethanol-blended gasoline (produced in association with NETs) has the potential to reduce impacts; however, achieving reductions similar to those of electric and all-ethanol vehicles, when compared to conventional gasoline, would depend on NETs that are unlikely to be implemented (e.g. BECCS from combustion). Estimates show that the carbon credits made available in RenovaBio will probably not be sufficient to provide attractive financial viability. Other instruments, such as private funding through the voluntary carbon market (VCM) and specific national incentive policies, may be essential for financing NETs.
巴西的国家生物燃料政策（RenovaBio）促进负排放技术（NETs）作为降低生物燃料碳强度（CI）的手段之一；然而，迄今为止，该国没有一家工厂将这种战略纳入其生产流程。本研究使用RenovaBio采用的方法计算了两种有前景的net（即具有碳捕获和储存（BECCS）的生物能源和生物炭的现场应用）对甘蔗乙醇CI的潜在影响。此外，评估使用了62家经认证的甘蔗厂提供的农业和工业阶段的数据。结果表明，这些策略有可能显著降低乙醇的CI。得到的水合乙醇+32.8 g CO2e/MJ（不考虑NETs）的值可以降至+15.9（使用1.0 t生物炭/ha）， +10.4（使用发酵产生的BECCS）和- 81.3（使用燃烧产生的BECCS）。乙醇混合汽油（与NETs一起生产）具有减少影响的潜力；然而，与传统汽油相比，实现与电动和全乙醇汽车类似的减排将取决于不太可能实施的净排放（例如燃烧产生的BECCS）。估计显示，RenovaBio提供的碳信用额可能不足以提供有吸引力的财务可行性。其他手段，例如通过自愿碳市场提供的私人资金和具体的国家奖励政策，可能是为网络提供资金的必要手段。
{"title":"Negative emission strategies to reduce the carbon intensity of Brazilian sugarcane ethanol under RenovaBio","authors":"Lucas G. Pereira , Marília Ieda da S. Folegatti , Nilza Patrícia Ramos , Cristiano Alberto de Andrade , Anna Leticia M.T. Pighinelli , Rosana Galindo , Joaquim E.A. Seabra","doi":"10.1016/j.cles.2025.100208","DOIUrl":"10.1016/j.cles.2025.100208","url":null,"abstract":"<div><div>Brazil’s National Biofuel Policy (RenovaBio) promotes negative emission technologies (NETs) as one of the instruments to reduce the carbon intensity (CI) of biofuels; however, no mill in the country has integrated such strategies into its production processes to date.</div><div>The present study examined the potential impact of two promising NETs (i.e, bioenergy with carbon capture and storage (BECCS) and field application of biochar) on the CI of sugarcane ethanol, calculated using the methodological approach adopted by RenovaBio. In addition, the assessment used data from the agricultural and industrial stages provided by 62 certified sugarcane mills.</div><div>Results show that these strategies have the potential to significantly reduce the CI of ethanol. The obtained value of +32.8 g CO<sub>2</sub>e/MJ of hydrated ethanol (without NETs considered) could be reduced to +15.9 (with the application of 1.0 t biochar/ha), +10.4 (with BECCS from fermentation), and −81.3 (with BECCS from combustion). Ethanol-blended gasoline (produced in association with NETs) has the potential to reduce impacts; however, achieving reductions similar to those of electric and all-ethanol vehicles, when compared to conventional gasoline, would depend on NETs that are unlikely to be implemented (e.g. BECCS from combustion). Estimates show that the carbon credits made available in RenovaBio will probably not be sufficient to provide attractive financial viability. Other instruments, such as private funding through the voluntary carbon market (VCM) and specific national incentive policies, may be essential for financing NETs.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"12 ","pages":"Article 100208"},"PeriodicalIF":0.0,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144861116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-05DOI: 10.1016/j.cles.2025.100206
Erlend Hordvei , Sebastian Emil Hummelen , Marianne Petersen , Stian Backe , Pedro Crespo del Granado
As the European countries strive to meet their ambitious climate goals, renewable hydrogen has emerged to aid in decarbonizing energy-intensive sectors and support the overall energy transition. To ensure that hydrogen production aligns with these goals, the European Commission has introduced criteria for additionality, temporal correlation, and geographical correlation. These criteria are designed to ensure that hydrogen production from renewable sources supports the growth of renewable energy. This study assesses the impact of these criteria on green hydrogen production, focusing on production costs and technology impacts. The European energy market is simulated up to 2048 using stochastic programming, applying these requirements exclusively to green hydrogen production without the phased-in compliance period outlined in the EU regulations. The findings show that meeting the criteria will increase expected system costs by €82 billion from 2024 to 2048, largely due to the rapid shift from fossil fuels to renewable energy. The additionality requirement, which mandates the use of new renewable energy installations for electrolysis, proves to be the most expensive, but also the most effective in accelerating renewable energy adoption.
{"title":"From policy to practice: Upper bound cost estimates of Europe ’s green hydrogen ambitions","authors":"Erlend Hordvei , Sebastian Emil Hummelen , Marianne Petersen , Stian Backe , Pedro Crespo del Granado","doi":"10.1016/j.cles.2025.100206","DOIUrl":"10.1016/j.cles.2025.100206","url":null,"abstract":"<div><div>As the European countries strive to meet their ambitious climate goals, renewable hydrogen has emerged to aid in decarbonizing energy-intensive sectors and support the overall energy transition. To ensure that hydrogen production aligns with these goals, the European Commission has introduced criteria for additionality, temporal correlation, and geographical correlation. These criteria are designed to ensure that hydrogen production from renewable sources supports the growth of renewable energy. This study assesses the impact of these criteria on green hydrogen production, focusing on production costs and technology impacts. The European energy market is simulated up to 2048 using stochastic programming, applying these requirements exclusively to green hydrogen production without the phased-in compliance period outlined in the EU regulations. The findings show that meeting the criteria will increase expected system costs by €82 billion from 2024 to 2048, largely due to the rapid shift from fossil fuels to renewable energy. The additionality requirement, which mandates the use of new renewable energy installations for electrolysis, proves to be the most expensive, but also the most effective in accelerating renewable energy adoption.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"12 ","pages":"Article 100206"},"PeriodicalIF":0.0,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144886444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rural electrification remains a significant development challenge in Southeast Asia, where over 45 million people still lack access to reliable electricity. This review uses a comparative analysis of empirical data and policy interventions across the region to evaluate the potential and barriers of renewable energy technologies (RETs) including solar, wind, biomass, and small-scale hydropower. The study aims to synthesize regional implementation outcomes, identify enabling frameworks, and highlight scalable hybrid solutions. Methodologically, over 100 published sources were reviewed to extract quantitative and qualitative data from key case studies in countries such as Vietnam, Indonesia, and the Philippines. Results show that solar PV systems, with a cost decline exceeding 80 % in the past decade, represent the most viable off-grid solution. Vietnam will achieve over 16 GW of installed capacity by 2022. Biomass energy contributes up to 15 % of rural energy use in Indonesia and Thailand, while small hydropower accounts for 20 % of rural generation in Laos and Vietnam. Hybrid renewable energy systems (HRES), integrating solar, wind, and biomass, reduce costs by up to 30 % compared to standalone systems and enhance supply reliability. However, deployment remains hindered by upfront costs (e.g., over $2500 per household for solar), limited technical expertise, policy inconsistencies, and socio-cultural resistance. The novelty of this review lies in its regional synthesis of RET policy impacts and its proposal of a diagnostic framework linking technology choice with socio-economic conditions. In conclusion, targeted subsidies, capacity-building, and community-driven models are crucial to overcoming barriers and unlocking RET's potential for inclusive, resilient, and sustainable rural electrification in Southeast Asia.
{"title":"Potential of renewable energy technologies for rural electrification in Southeast Asia: A review","authors":"Rizalman Mamat , Mohd Fairusham Ghazali , Erdiwansyah , S.M. Rosdi","doi":"10.1016/j.cles.2025.100207","DOIUrl":"10.1016/j.cles.2025.100207","url":null,"abstract":"<div><div>Rural electrification remains a significant development challenge in Southeast Asia, where over 45 million people still lack access to reliable electricity. This review uses a comparative analysis of empirical data and policy interventions across the region to evaluate the potential and barriers of renewable energy technologies (RETs) including solar, wind, biomass, and small-scale hydropower. The study aims to synthesize regional implementation outcomes, identify enabling frameworks, and highlight scalable hybrid solutions. Methodologically, over 100 published sources were reviewed to extract quantitative and qualitative data from key case studies in countries such as Vietnam, Indonesia, and the Philippines. Results show that solar PV systems, with a cost decline exceeding 80 % in the past decade, represent the most viable off-grid solution. Vietnam will achieve over 16 GW of installed capacity by 2022. Biomass energy contributes up to 15 % of rural energy use in Indonesia and Thailand, while small hydropower accounts for 20 % of rural generation in Laos and Vietnam. Hybrid renewable energy systems (HRES), integrating solar, wind, and biomass, reduce costs by up to 30 % compared to standalone systems and enhance supply reliability. However, deployment remains hindered by upfront costs (e.g., over $2500 per household for solar), limited technical expertise, policy inconsistencies, and socio-cultural resistance. The novelty of this review lies in its regional synthesis of RET policy impacts and its proposal of a diagnostic framework linking technology choice with socio-economic conditions. In conclusion, targeted subsidies, capacity-building, and community-driven models are crucial to overcoming barriers and unlocking RET's potential for inclusive, resilient, and sustainable rural electrification in Southeast Asia.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"12 ","pages":"Article 100207"},"PeriodicalIF":0.0,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144704875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study focuses on the development of a hybrid renewable energy system, with a battery energy storage system, for a small island, Koh Hang, off the coast of Krabi province, in the Andaman Sea of Thailand. Currently un-powered, the island has a good solar energy potential, but limited wind energy potential. Using HOMER Pro optimization model, seven hybrid renewable energy systems consisting of solar PV, wind, biogas, and a battery energy storage system (BESS) are studied to identify the optimal configuration to meet the load demand with the lowest levelized cost of energy (LCOE). Among the hybrid configurations studied, the optimal solar PV – biogas - BESS system offered the lowest LCOE of 0.215 US$/kWh. A public opinion survey was also carried out in the community to measure the level of acceptance of such system on the island, with the willingness to pay for a proposed tariff being the key issue for the long-term sustainability of the proposed system. This work, which can be replicated in similar off-grid microgrids, contribute in improving the quality of life and the economy of off-grid settlements, while minimizing the impacts on the environment.
{"title":"Sustainability analysis of hybrid renewable-based power generation with battery energy storage system for remote islands: Application to Koh Hang, Thailand","authors":"Weerasak Chaichan , Jompob Waewsak , Yoawapa Naklua , Fida Ali , Chokchai Mueanmas , Ruamporn Nikhom , Chuleerat Kongruang , Yves Gagnon","doi":"10.1016/j.cles.2025.100203","DOIUrl":"10.1016/j.cles.2025.100203","url":null,"abstract":"<div><div>This study focuses on the development of a hybrid renewable energy system, with a battery energy storage system, for a small island, Koh Hang, off the coast of Krabi province, in the Andaman Sea of Thailand. Currently un-powered, the island has a good solar energy potential, but limited wind energy potential. Using HOMER Pro optimization model, seven hybrid renewable energy systems consisting of solar PV, wind, biogas, and a battery energy storage system (BESS) are studied to identify the optimal configuration to meet the load demand with the lowest levelized cost of energy (LCOE). Among the hybrid configurations studied, the optimal solar PV – biogas - BESS system offered the lowest LCOE of 0.215 US$/kWh. A public opinion survey was also carried out in the community to measure the level of acceptance of such system on the island, with the willingness to pay for a proposed tariff being the key issue for the long-term sustainability of the proposed system. This work, which can be replicated in similar off-grid microgrids, contribute in improving the quality of life and the economy of off-grid settlements, while minimizing the impacts on the environment.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"12 ","pages":"Article 100203"},"PeriodicalIF":0.0,"publicationDate":"2025-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144704876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-16DOI: 10.1016/j.cles.2025.100202
Pascalin Tiam Kapen
Advancing optimization methodologies is crucial for addressing the complex challenges of real-world energy systems, particularly those involving high-dimensional search spaces. This work introduces the Caracal Optimization Algorithm (CAO), a novel metaheuristic inspired by the hunting behavior of caracals, known for their precision, agility, and adaptability. By mimicking the caracal's stealthy stalking, explosive leaps, and dynamic adjustments to prey movement, the algorithm incorporates chaotic exploration mechanisms and adaptive leap strategies, effectively balancing global search diversity and local solution refinement. This innovation enables the CAO to navigate intricate solution landscapes, avoid local optima, and achieve rapid convergence. The CAO was applied to optimize the sizing of off-grid hybrid energy systems, particularly Wind/Photovoltaic/Battery configurations, focusing on key metrics such as loss of power supply probability (LPSP), net present cost (NPC), and levelized cost of energy (LCOE). The algorithm was benchmarked against four established metaheuristic methods, Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), Zebra Optimization Algorithm (ZOA), and Particle Swarm Optimization (PSO). Comparative analyses showed that CAO outperforms these benchmarks, achieving the best solution quality, faster convergence, and significantly reduced computational time. Notably, CAO reduced the LCOE to 0.1069 US$/kWh, the NPC to approximately US$ 50,874, and demonstrated superior energy cost optimization with faster convergence compared to other algorithms. The findings also highlighted significant variability in photovoltaic power output, peaking at 20 kW during high solar radiation, reflecting the intermittent nature of solar energy. Wind turbine power showed more consistency, peaking at 12 kW. Battery charging and discharging exhibited fluctuations based on weather, time of day, and seasonal changes. The analysis revealed that lower LCOE values occur under favorable financial conditions, such as low inflation and interest rates. Conversely, higher LCOE values were observed with increased inflation and interest rates, emphasizing the need for minimizing these financial factors for cost-effective energy generation. These results underline the Caracal Optimization Algorithm's potential to enhance hybrid renewable energy systems, offering a cleaner, more cost-effective solution. This study not only demonstrates the effectiveness of CAO in optimizing energy systems but also highlights its adaptability in addressing complex, multi-objective optimization problems, proving its capability to navigate high-dimensional spaces efficiently.
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Pub Date : 2025-07-15DOI: 10.1016/j.cles.2025.100201
Christoph Schilling , Sheng H. Xie , Blas Mola-Yudego , Hisham Zerriffi , Christopher Gaston , Dominik Roeser
This study evaluates the sustainability impacts of small-scale biomass combined heat and power (CHP) systems in remote communities, focusing on the case of Kwadacha, a remote Indigenous community in British Columbia. The analysis compares the biomass CHP system implemented in 2016 with the community’s previous diesel power and propane heating systems, examining economic, social, and environmental dimensions while exploring the factors that led to the project’s cessation in 2021.
The biomass CHP system demonstrated a 5.15-fold increase in local employment, a 2.76-fold rise in community income, and an annual greenhouse gas emissions avoidance of 1113 tCO₂e. It also achieved a notable supply chain cost advantage, with the cost of biomass transport and processing being approximately one-third that of diesel and propane delivery. However, high operational costs, escalating maintenance issues, and persistent labor shortages posed major barriers to long-term viability. These challenges were compounded by entrenched diesel subsidies, which created economic disincentives for renewable energy adoption despite clear sustainability gains.
The findings highlight the potential of biomass CHP systems to contribute to wildfire mitigation, rural economic development, and decarbonization in forested, off-grid communities. However, realizing these benefits requires policy realignment, sustained technical support, and integrated funding mechanisms. The Kwadacha project provides critical lessons for future deployments, emphasizing the need for context-specific strategies that balance economic, environmental, and social goals in the implementation of renewable energy systems.
{"title":"Small-scale biomass combined heat and power systems in remote indigenous communities: Economic, social and environmental sustainability challenges amid policy misalignment","authors":"Christoph Schilling , Sheng H. Xie , Blas Mola-Yudego , Hisham Zerriffi , Christopher Gaston , Dominik Roeser","doi":"10.1016/j.cles.2025.100201","DOIUrl":"10.1016/j.cles.2025.100201","url":null,"abstract":"<div><div>This study evaluates the sustainability impacts of small-scale biomass combined heat and power (CHP) systems in remote communities, focusing on the case of Kwadacha, a remote Indigenous community in British Columbia. The analysis compares the biomass CHP system implemented in 2016 with the community’s previous diesel power and propane heating systems, examining economic, social, and environmental dimensions while exploring the factors that led to the project’s cessation in 2021.</div><div>The biomass CHP system demonstrated a 5.15-fold increase in local employment, a 2.76-fold rise in community income, and an annual greenhouse gas emissions avoidance of 1113 tCO₂e. It also achieved a notable supply chain cost advantage, with the cost of biomass transport and processing being approximately one-third that of diesel and propane delivery. However, high operational costs, escalating maintenance issues, and persistent labor shortages posed major barriers to long-term viability. These challenges were compounded by entrenched diesel subsidies, which created economic disincentives for renewable energy adoption despite clear sustainability gains.</div><div>The findings highlight the potential of biomass CHP systems to contribute to wildfire mitigation, rural economic development, and decarbonization in forested, off-grid communities. However, realizing these benefits requires policy realignment, sustained technical support, and integrated funding mechanisms. The Kwadacha project provides critical lessons for future deployments, emphasizing the need for context-specific strategies that balance economic, environmental, and social goals in the implementation of renewable energy systems.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"12 ","pages":"Article 100201"},"PeriodicalIF":0.0,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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