Pub Date : 2024-10-01DOI: 10.1016/j.ecmx.2024.100758
A. Hosseinpour
Hybrid electrical vehicles (HEV) should be designed somehow torque is smooth. Because torque ripple not only reduces control precision but also increases elements vibration that causes acoustic noise, mechanical instability and early aging parts. Furthermore, torque per volume should be maximized and heat removal should be accomplished without torque weakening. It is proposed the volume and internal dimensions are determined due to the thermal considerations and maximize torque per volume. The mentioned application is neglected heat removal so volume is constant. Therefore, HEV is manufactured by two objective functions: either minimum fluctuations or maximum average torque. In this paper series hybrid excitation synchronous machine (SHESM) is utilized as HEV. Two-objective optimization problems are solved by MOEA/D, NSGA II, PESA II and SPEA II algorithms based on a two-dimensional (2-D) model. The performance indices of optimal structure are evaluated by 2-D and confirmed by numerical method.
混合动力汽车（HEV）在设计时应确保扭矩平稳。因为扭矩波纹不仅会降低控制精度，还会增加元件振动，从而导致噪音、机械不稳定和零件早期老化。此外，应最大限度地提高单位体积的扭矩，并在不削弱扭矩的情况下实现散热。建议根据热量因素确定体积和内部尺寸，并使单位体积扭矩最大化。上述应用忽略了散热，因此体积不变。因此，混合动力车的制造有两个目标函数：最小波动或最大平均扭矩。本文采用串联混合励磁同步机（SHESM）作为 HEV。基于二维（2-D）模型，采用 MOEA/D、NSGA II、PESA II 和 SPEA II 算法解决了双目标优化问题。优化结构的性能指标通过二维模型进行了评估，并通过数值方法进行了确认。
{"title":"Torque ripple reduction and increasing of torque per volume for hybrid electrical vehicle","authors":"A. Hosseinpour","doi":"10.1016/j.ecmx.2024.100758","DOIUrl":"10.1016/j.ecmx.2024.100758","url":null,"abstract":"<div><div>Hybrid electrical vehicles (HEV) should be designed somehow torque is smooth. Because torque ripple not only reduces control precision but also increases elements vibration that causes acoustic noise, mechanical instability and early aging parts. Furthermore, torque per volume should be maximized and heat removal should be accomplished without torque weakening. It is proposed the volume and internal dimensions are determined due to the thermal considerations and maximize torque per volume. The mentioned application is neglected heat removal so volume is constant. Therefore, HEV is manufactured by two objective functions: either minimum fluctuations or maximum average torque. In this paper series hybrid excitation synchronous machine (SHESM) is utilized as HEV. Two-objective optimization problems are solved by MOEA/D, NSGA II, PESA II and SPEA II algorithms based on a two-dimensional (2-D) model. The performance indices of optimal structure are evaluated by 2-D and confirmed by numerical method.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"24 ","pages":"Article 100758"},"PeriodicalIF":7.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142540333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01DOI: 10.1016/j.ecmx.2024.100765
Shun Nakayama , Wanglin Yan , Amane Fujita
High-performance buildings (HPBs) are designed to minimize environmental impacts during operation, but ensuring continuous efficiency improvements remains a challenge. Existing energy audit methodologies have been developed with limited support of precise operational data. However, the advent of Building Energy Management Systems (BEMS) and the establishment of de facto industry standards for building service and space usage have enabled energy audits to be conducted at an unprecedented level of detail. Taking advantage of these developments, this study proposes an original integrated approach for HPB by combining a standard energy audit framework with BEMS data. The novel method conducts: (1) detailed energy and water consumption profiling across multiple timescales; (2) benchmarking using data envelopment analysis against other HPBs; (3) building diagnostics to identify further carbon reduction opportunities; and (4) marginal abatement cost analysis to explore economically feasible improvement measures for owners. When specifically applied to an HPB in Tokyo, the findings reveal substantial room for further improvements. At least 10.1% in energy saving potential exists compared to the building’s design performance. Moreover, implementing selected cost-effective measures could economically achieve an 8.9% reduction in CO2 emissions. This multifaceted study makes three key original contributions. First, it develops a systematic energy audit methodology tailored to BEMS-equipped HPBs, enabling granular, spatiotemporal analysis of resource consumption. Second, it extends this framework beyond energy to holistically encompass water consumption. Third, it provides quantitative evidence that even highly-rated HPBs may still have significant remaining potential for operational environmental impact reductions, which can be identified in detail through the proposed approach. Overall, by harnessing BEMS data and industry standards, this research demonstrates a feasible and cost-effective pathway for HPB owners and operators to continuously optimize resource efficiency. As the urgency of climate action intensifies, this innovative approach offers a crucial toolkit for the building sector to enhance its contribution to global sustainability goals.
{"title":"Developing an energy audit methodology for assessing decarbonization potential in high performance buildings","authors":"Shun Nakayama , Wanglin Yan , Amane Fujita","doi":"10.1016/j.ecmx.2024.100765","DOIUrl":"10.1016/j.ecmx.2024.100765","url":null,"abstract":"<div><div>High-performance buildings (HPBs) are designed to minimize environmental impacts during operation, but ensuring continuous efficiency improvements remains a challenge. Existing energy audit methodologies have been developed with limited support of precise operational data. However, the advent of Building Energy Management Systems (BEMS) and the establishment of de facto industry standards for building service and space usage have enabled energy audits to be conducted at an unprecedented level of detail. Taking advantage of these developments, this study proposes an original integrated approach for HPB by combining a standard energy audit framework with BEMS data. The novel method conducts: (1) detailed energy and water consumption profiling across multiple timescales; (2) benchmarking using data envelopment analysis against other HPBs; (3) building diagnostics to identify further carbon reduction opportunities; and (4) marginal abatement cost analysis to explore economically feasible improvement measures for owners. When specifically applied to an HPB in Tokyo, the findings reveal substantial room for further improvements. At least 10.1% in energy saving potential exists compared to the building’s design performance. Moreover, implementing selected cost-effective measures could economically achieve an 8.9% reduction in CO2 emissions. This multifaceted study makes three key original contributions. First, it develops a systematic energy audit methodology tailored to BEMS-equipped HPBs, enabling granular, spatiotemporal analysis of resource consumption. Second, it extends this framework beyond energy to holistically encompass water consumption. Third, it provides quantitative evidence that even highly-rated HPBs may still have significant remaining potential for operational environmental impact reductions, which can be identified in detail through the proposed approach. Overall, by harnessing BEMS data and industry standards, this research demonstrates a feasible and cost-effective pathway for HPB owners and operators to continuously optimize resource efficiency. As the urgency of climate action intensifies, this innovative approach offers a crucial toolkit for the building sector to enhance its contribution to global sustainability goals.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"24 ","pages":"Article 100765"},"PeriodicalIF":7.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142540334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes a procedure to build a Time of the Week AutoRegressive eXogenous (TOW-ARX) model, indexed with respect to time and day of the week, to characterize heat consumption in tertiary buildings. Models for building heat load characterization and prediction are crucial to enhance energy efficiency. The proposed model can be used for different purposes, e.g., control of indoor climate, or characterization of the thermal response of the building. A case study is described where the TOW-ARX model is used to characterize the energy consumption of a large retail building in Madrid. In order to discard the risk of model overfitting, cross validation is applied using the k-fold technique. The performance of the TOW-ARX model is compared with a set of different models: a reduced version of the model where similar segments are clustered using the k-means method (R-TOW-ARX), a general ARX model, a linear regression steady-state TOW model (TOW-LR), a version of the latter reduced through clustering (R-TOW-LR), and a general multiple linear regression model (LR). The results reveal that ARX-based models notably outperforms the rest. The TOW-ARX model shows the best metrics, but also outnumbers the number of coefficients of the other models by far. The selection of the most suitable model is not straightforward and should depend on the purpose of such model: the TOW-ARX model would arguably be the best for control purposes due to its low mean absolute error, but the ARX model would be preferable for an efficient characterization of the thermal response of a building due to its reduced number of parameters.
{"title":"Time of the week AutoRegressive eXogenous (TOW-ARX) model to predict thermal consumption in a large commercial mall","authors":"Iñigo Lopez-Villamor , Olaia Eguiarte , Beñat Arregi , Roberto Garay-Martinez , Antonio Garrido-Marijuan","doi":"10.1016/j.ecmx.2024.100777","DOIUrl":"10.1016/j.ecmx.2024.100777","url":null,"abstract":"<div><div>This paper proposes a procedure to build a Time of the Week AutoRegressive eXogenous (TOW-ARX) model, indexed with respect to time and day of the week, to characterize heat consumption in tertiary buildings. Models for building heat load characterization and prediction are crucial to enhance energy efficiency. The proposed model can be used for different purposes, e.g., control of indoor climate, or characterization of the thermal response of the building. A case study is described where the TOW-ARX model is used to characterize the energy consumption of a large retail building in Madrid. In order to discard the risk of model overfitting, cross validation is applied using the k-fold technique. The performance of the TOW-ARX model is compared with a set of different models: a reduced version of the model where similar segments are clustered using the k-means method (R-TOW-ARX), a general ARX model, a linear regression steady-state TOW model (TOW-LR), a version of the latter reduced through clustering (R-TOW-LR), and a general multiple linear regression model (LR). The results reveal that ARX-based models notably outperforms the rest. The TOW-ARX model shows the best metrics, but also outnumbers the number of coefficients of the other models by far. The selection of the most suitable model is not straightforward and should depend on the purpose of such model: the TOW-ARX model would arguably be the best for control purposes due to its low mean absolute error, but the ARX model would be preferable for an efficient characterization of the thermal response of a building due to its reduced number of parameters.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"24 ","pages":"Article 100777"},"PeriodicalIF":7.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142561339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01DOI: 10.1016/j.ecmx.2024.100775
Yohanis Tangke Tosuli , Cahyadi , Hafif Dafiqurrohman , Rudi Hermawan , Adi Surjosatyo
Sago-based foods have become a staple food in eastern Indonesia. Sago waste, as a by-product, has the potential to be used as a renewable energy fuel. This research aims to use sago dregs waste as an energy source by converting it into renewable energy using Top Lit Updraft (TULD) fixed bed gasification. Al2O3, a potential solid waste derived from coal fly ash, is also being investigated for use in sago dreg pellets.The two various Al2O3 contents in sago dreg pellets that will be examined with 5 % Al2O3 and 10 % Al2O3. Operational parameters employed in the gasification process involving the TULD reactor, such as gasification temperature, air flow rate, air-to-fuel ratio (AFR), and syngas assessment. According to the results, adding Al2O3 as a catalyst to sago dreg pellets can improve syngas production (H2, CO, and CH4). The most significant alteration is that the average hydrogen gas (H2) content has increased, with the greatest being in 5 % Al2O3 with 31.65 %, and 10 % Al2O3 with 29.94 %. Meanwhile, the CO and CH4 gas content was found to be highest at 10 % Al2O3, with each experiencing an increase in average (CO 4.33 % and CH4 26.45 %) as compared to sago dregs pellets without Al2O3. Finally, sago dregs pellets with an Al2O3 catalyst have a high potential as an alternative energy fuel for internal combustion engines with H2/CO of 1.65 and 1.51 respectively, for 5 % Al2O3 and 10 % Al2O3, with low tar content.
{"title":"Gasification of sago dreg waste in a top-lit updraft fixed bed gasifier: Syngas composition and its effect with additional Al2O3 as catalyst","authors":"Yohanis Tangke Tosuli , Cahyadi , Hafif Dafiqurrohman , Rudi Hermawan , Adi Surjosatyo","doi":"10.1016/j.ecmx.2024.100775","DOIUrl":"10.1016/j.ecmx.2024.100775","url":null,"abstract":"<div><div>Sago-based foods have become a staple food in eastern Indonesia. Sago waste, as a by-product, has the potential to be used as a renewable energy fuel. This research aims to use sago dregs waste as an energy source by converting it into renewable energy using Top Lit Updraft (TULD) fixed bed gasification. Al<sub>2</sub>O<sub>3</sub>, a potential solid waste derived from coal fly ash, is also being investigated for use in sago dreg pellets.The two various Al<sub>2</sub>O<sub>3</sub> contents in sago dreg pellets that will be examined with 5 % Al<sub>2</sub>O<sub>3</sub> and 10 % Al<sub>2</sub>O<sub>3</sub>. Operational parameters employed in the gasification process involving the TULD reactor, such as gasification temperature, air flow rate, air-to-fuel ratio (AFR), and syngas assessment. According to the results, adding Al<sub>2</sub>O<sub>3</sub> as a catalyst to sago dreg pellets can improve syngas production (H<sub>2</sub>, CO, and CH<sub>4</sub>). The most significant alteration is that the average hydrogen gas (H<sub>2</sub>) content has increased, with the greatest being in 5 % Al<sub>2</sub>O<sub>3</sub> with 31.65 %, and 10 % Al<sub>2</sub>O<sub>3</sub> with 29.94 %. Meanwhile, the CO and CH<sub>4</sub> gas content was found to be highest at 10 % Al<sub>2</sub>O<sub>3</sub>, with each experiencing an increase in average (CO 4.33 % and CH<sub>4</sub> 26.45 %) as compared to sago dregs pellets without Al<sub>2</sub>O<sub>3</sub>. Finally, sago dregs pellets with an Al<sub>2</sub>O<sub>3</sub> catalyst have a high potential as an alternative energy fuel for internal combustion engines with H<sub>2</sub>/CO of 1.65 and 1.51 respectively, for 5 % Al<sub>2</sub>O<sub>3</sub> and 10 % Al<sub>2</sub>O<sub>3</sub>, with low tar content.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"24 ","pages":"Article 100775"},"PeriodicalIF":7.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The rapid increase in biomass and plastic waste poses significant environmental challenges. Co-pyrolysis of biomass with plastic wastes offers a promising avenue for sustainable waste management and renewable energy generation. This study covers several novel aspects: First, it investigates the impacts of feedstock composition and operating conditions in pyrolysis (individual feedstock) and co-pyrolysis (biomass and plastic wastes). The study reveals that synergistic effects, specifically improved yields and optimized temperature, exist in the co-pyrolysis of biomass and plastic wastes compared to individual feedstock. Secondly, a suitable blended machine learning predictive model (with Random Forest, Gradient Boosting Regressor, and XGBoost) and robust optimization framework are developed to address model accuracy, non-linear interactions, and uncertainties in pyrolysis such as temperature, heating rate, and biomass-to-plastic ratio. This study predicts the bio-oil yield quantitatively (amount) and qualitatively (composition) with high accuracy (R2 > 0.97). Thirdly, key factors contributing to yield include plastic content (18 %) and biomass type (13 %) have been identified through Gini feature importance and Shapley Additive Explanation (SHAP) analysis. Furthermore, multi-objective optimization techniques reveal the most optimal bio-oil yield under specific conditions, supported by uncertainty analysis, which confines bio-oil yield to a range of 30–50 %. Finally, it also demonstrates a case study to find the optimal bio-oil yield and quality conditions using co-pyrolysis of local resources, i.e., biomass (wood and bagasse) and plastic wastes. The case study suggests optimal conditions like > 50 °C heating rate, <50 min pyrolysis time, and > 60 % plastic content in a blend of wood and HDPE. This study assists industries and policymakers to assess and understand the viability of co-pyrolysis, optimal design parameters, and process impacts.
{"title":"Optimizing pyrolysis and Co-Pyrolysis of plastic and biomass using Artificial Intelligence","authors":"Manish Sharma Timilsina , Yuvraj Chaudhary , Prikshya Bhattarai , Bibek Uprety , Dilip Khatiwada","doi":"10.1016/j.ecmx.2024.100783","DOIUrl":"10.1016/j.ecmx.2024.100783","url":null,"abstract":"<div><div>The rapid increase in biomass and plastic waste poses significant environmental challenges. Co-pyrolysis of biomass with plastic wastes offers a promising avenue for sustainable waste management and renewable energy generation. This study covers several novel aspects: First, it investigates the impacts of feedstock composition and operating conditions in pyrolysis (individual feedstock) and co-pyrolysis (biomass and plastic wastes). The study reveals that synergistic effects, specifically improved yields and optimized temperature, exist in the co-pyrolysis of biomass and plastic wastes compared to individual feedstock. Secondly, a suitable blended machine learning predictive model (with Random Forest, Gradient Boosting Regressor, and XGBoost) and robust optimization framework are developed to address model accuracy, non-linear interactions, and uncertainties in pyrolysis such as temperature, heating rate, and biomass-to-plastic ratio. This study predicts the bio-oil yield quantitatively (amount) and qualitatively (composition) with high accuracy (R<sup>2</sup> > 0.97). Thirdly, key factors contributing to yield include plastic content (18 %) and biomass type (13 %) have been identified through Gini feature importance and Shapley Additive Explanation (SHAP) analysis. Furthermore, multi-objective optimization techniques reveal the most optimal bio-oil yield under specific conditions, supported by uncertainty analysis, which confines bio-oil yield to a range of 30–50 %. Finally, it also demonstrates a case study to find the optimal bio-oil yield and quality conditions using co-pyrolysis of local resources, i.e., biomass (wood and bagasse) and plastic wastes. The case study suggests optimal conditions like > 50 °C heating rate, <50 min pyrolysis time, and > 60 % plastic content in a blend of wood and HDPE. This study assists industries and policymakers to assess and understand the viability of co-pyrolysis, optimal design parameters, and process impacts.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"24 ","pages":"Article 100783"},"PeriodicalIF":7.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The rising global demand for clean and renewable energy has intensified interest in hydrokinetic energy harvesting, with Savonius turbines gaining attention due to their simplicity and low cost. While numerous studies have focused on refining blade designs for wind turbines, limited research has been conducted on water turbines to identify the best design. This study investigates the effect of blade geometry on the efficiency of Savonius hydrokinetic turbines to identify the optimal configuration. Three new blade designs were tested, incorporating inner blades and varying blade numbers. These designs were experimentally evaluated to identify the optimal turbine configuration for maximum efficiency, and the findings were then validated through numerical studies. Rotational analysis was conducted to investigate torque variations across a full turbine rotation from 0° to 360°, and flow characteristic analysis was performed by utilizing pressure and contour plots at critical positions, including 0°, minimum torque coefficient (CT Min), and maximum torque coefficient (CT Max). Results indicate that the 2-blade Savonius turbine achieved the highest efficiency, with a maximum torque coefficient of 0.29 and a power coefficient of 0.22. It demonstrated 63.5 % greater power efficiency compared to the 3-Blade Savonius Turbine, 2.65 times greater than the Segmented Quarter Savonius Turbine, and 2.26 times greater than the Concentric Arc Savonius Turbine. These findings highlight the importance of blade geometry optimization in improving the performance of Savonius turbines for efficient hydrokinetic energy generation.
{"title":"Comprehensive analysis of blade geometry effects on Savonius hydrokinetic turbine efficiency: Pathways to clean energy","authors":"Shanegowda T.G. , C.M. Shashikumar , Veershetty Gumtapure , Vasudeva Madav","doi":"10.1016/j.ecmx.2024.100762","DOIUrl":"10.1016/j.ecmx.2024.100762","url":null,"abstract":"<div><div>The rising global demand for clean and renewable energy has intensified interest in hydrokinetic energy harvesting, with Savonius turbines gaining attention due to their simplicity and low cost. While numerous studies have focused on refining blade designs for wind turbines, limited research has been conducted on water turbines to identify the best design. This study investigates the effect of blade geometry on the efficiency of Savonius hydrokinetic turbines to identify the optimal configuration. Three new blade designs were tested, incorporating inner blades and varying blade numbers. These designs were experimentally evaluated to identify the optimal turbine configuration for maximum efficiency, and the findings were then validated through numerical studies. Rotational analysis was conducted to investigate torque variations across a full turbine rotation from 0° to 360°, and flow characteristic analysis was performed by utilizing pressure and contour plots at critical positions, including 0°, minimum torque coefficient (C<sub>T Min</sub>), and maximum torque coefficient (C<sub>T Max</sub>). Results indicate that the 2-blade Savonius turbine achieved the highest efficiency, with a maximum torque coefficient of 0.29 and a power coefficient of 0.22. It demonstrated 63.5 % greater power efficiency compared to the 3-Blade Savonius Turbine, 2.65 times greater than the Segmented Quarter Savonius Turbine, and 2.26 times greater than the Concentric Arc Savonius Turbine. These findings highlight the importance of blade geometry optimization in improving the performance of Savonius turbines for efficient hydrokinetic energy generation.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"24 ","pages":"Article 100762"},"PeriodicalIF":7.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01DOI: 10.1016/j.ecmx.2024.100761
Pengfei Hu, Liqun Qian, Zimeng Li, Yanxue Yu, Dong Wang
This paper proposes a biogas-wind-solar-hydrogen multi-microgrid system to address the issues of poor economy and reliability, as well as the waste of wind and solar energy, in single energy-based isolated microgrid systems. The study considers the coupling constraints of multiple time scales and establishes a dynamic economic dispatch model. Furthermore, a consensus-based distributed dynamic economic dispatch strategy is proposed. To tackle the challenge of unified economic dispatch caused by the interaction among multiple microgrids in the joint operation of the biogas-wind-solar-hydrogen multi-microgrid system, a microgrid selfishness impact model and elimination strategy are developed. Simulation results demonstrate the effectiveness and superiority of the proposed distributed dynamic economic dispatch strategy considering individual selfishness in the biogas-wind-solar-hydrogen multi-microgrid system.
{"title":"Distributed dynamic economic dispatch of biogas-wind-solar-hydrogen multi-microgrid system considering individual selfishness","authors":"Pengfei Hu, Liqun Qian, Zimeng Li, Yanxue Yu, Dong Wang","doi":"10.1016/j.ecmx.2024.100761","DOIUrl":"10.1016/j.ecmx.2024.100761","url":null,"abstract":"<div><div>This paper proposes a biogas-wind-solar-hydrogen multi-microgrid system to address the issues of poor economy and reliability, as well as the waste of wind and solar energy, in single energy-based isolated microgrid systems. The study considers the coupling constraints of multiple time scales and establishes a dynamic economic dispatch model. Furthermore, a consensus-based distributed dynamic economic dispatch strategy is proposed. To tackle the challenge of unified economic dispatch caused by the interaction among multiple microgrids in the joint operation of the biogas-wind-solar-hydrogen multi-microgrid system, a microgrid selfishness impact model and elimination strategy are developed. Simulation results demonstrate the effectiveness and superiority of the proposed distributed dynamic economic dispatch strategy considering individual selfishness in the biogas-wind-solar-hydrogen multi-microgrid system.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"24 ","pages":"Article 100761"},"PeriodicalIF":7.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142540341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The hydrothermal liquefaction (HTL) technique for liquefying lignocellulose biomass feedstock is often associated with low biocrude yield and poor fuel properties. This study examined the HTL of southern yellow pine sawdust and the hydrotreatment (HYD) of produced biocrudes in an effort to address these challenges. Pine HTL treatment was performed within water and water–ethanol mixed reaction medium at 250, 300, and 350℃ temperatures using metallic iron (Fe) as a catalyst. The rising reaction temperature in a water medium and increasing ethanol content in a mixed reaction medium were found to be effective in enhancing the biocrude yield from the non-catalytic pine HTL process. Maximum non-catalytic biocrude yield of 18 wt.% was produced in water at 350℃, whereas the ethanol and water (1:1 on mass basis) mixture generated the highest biocrude yield of 34 wt.% at 300℃ without any catalyst. The iron catalyst facilitated a maximum of 29 wt.% of biocrude yield as opposed to 18 wt.% without the catalyst at 350℃ in water. The use of an iron catalyst also raised the calorific value of produced biocrudes by 2.5–14 % within 250-350℃ in both water and water–ethanol media. The catalytic and non-catalytic biocrude products were chosen to undergo HYD treatment at 400 °C under high hydrogen pressure (initial 1000 psi) using an alumina-supported cobalt-molybdenum catalyst. The HYD treatment reduced the oxygen content of upgraded oils by 36–60 % compared to the parent HTL biocrudes with 35–37 MJ/kg calorific values. The simulated distillation detected the maximum gasoline range compounds in upgraded oil from catalyst and water–ethanol conditions, whereas the GC–MS analysis revealed the production of increased aromatic hydrocarbons in all upgraded HYD oils. This work has demonstrated the potential of ethanol and inexpensive iron catalyst in enhancing the biocrude production from pine, which could be upgraded to better fuel using the HYD process.
用于液化木质纤维素生物质原料的水热液化（HTL）技术往往与生物原油产量低和燃料性能差有关。本研究考察了南方黄松锯屑的水热液化和所生产生物原油的加氢处理 (HYD)，旨在解决这些难题。以金属铁（Fe）为催化剂，在 250、300 和 350℃的水和水乙醇混合反应介质中进行了松木热液化处理。研究发现，提高水介质中的反应温度和增加混合反应介质中的乙醇含量可有效提高非催化松木热液化工艺的生物原油产量。水介质在 350℃时产生的非催化生物原油产量最高，为 18%，而乙醇和水（质量比为 1:1）混合物在 300℃时产生的生物原油产量最高，为 34%，且不含任何催化剂。在 350℃的水中，铁催化剂可使生物原油产量达到最高的 29 重量百分比，而不使用催化剂时仅为 18 重量百分比。在 250-350℃ 的水介质和水乙醇介质中，使用铁催化剂还可将生产的生物原油的热值提高 2.5-14%。催化和非催化生物原油产品被选中在 400 °C 的高压氢气环境下（初始压力为 1000 psi），使用氧化铝支撑的钴钼催化剂进行 HYD 处理。与热值为 35-37 兆焦耳/千克的母 HTL 生物馏分油相比，HYD 处理使升级油的氧含量降低了 36-60%。模拟蒸馏检测出催化剂和水-乙醇条件下的升级油中汽油范围的化合物最多，而气相色谱-质谱分析表明，所有 HYD 升级油中产生的芳香烃都有所增加。这项工作证明了乙醇和廉价铁催化剂在提高松木生物原油产量方面的潜力，松木生物原油可通过 HYD 工艺升级为更好的燃料。
{"title":"Hydrothermal liquefaction of southern yellow pine with downstream processing for improved fuel grade chemicals production","authors":"Tawsif Rahman , Hossein Jahromi , Poulami Roy , Bijoy Biswas , Sushil Adhikari","doi":"10.1016/j.ecmx.2024.100735","DOIUrl":"10.1016/j.ecmx.2024.100735","url":null,"abstract":"<div><div>The hydrothermal liquefaction (HTL) technique for liquefying lignocellulose biomass feedstock is often associated with low biocrude yield and poor fuel properties. This study examined the HTL of southern yellow pine sawdust and the hydrotreatment (HYD) of produced biocrudes in an effort to address these challenges. Pine HTL treatment was performed within water and water–ethanol mixed reaction medium at 250, 300, and 350℃ temperatures using metallic iron (Fe) as a catalyst. The rising reaction temperature in a water medium and increasing ethanol content in a mixed reaction medium were found to be effective in enhancing the biocrude yield from the non-catalytic pine HTL process. Maximum non-catalytic biocrude yield of 18 wt.% was produced in water at 350℃, whereas the ethanol and water (1:1 on mass basis) mixture generated the highest biocrude yield of 34 wt.% at 300℃ without any catalyst. The iron catalyst facilitated a maximum of 29 wt.% of biocrude yield as opposed to 18 wt.% without the catalyst at 350℃ in water. The use of an iron catalyst also raised the calorific value of produced biocrudes by 2.5–14 % within 250-350℃ in both water and water–ethanol media. The catalytic and non-catalytic biocrude products were chosen to undergo HYD treatment at 400 °C under high hydrogen pressure (initial 1000 psi) using an alumina-supported cobalt-molybdenum catalyst. The HYD treatment reduced the oxygen content of upgraded oils by 36–60 % compared to the parent HTL biocrudes with 35–37 MJ/kg calorific values. The simulated distillation detected the maximum gasoline range compounds in upgraded oil from catalyst and water–ethanol conditions, whereas the GC–MS analysis revealed the production of increased aromatic hydrocarbons in all upgraded HYD oils. This work has demonstrated the potential of ethanol and inexpensive iron catalyst in enhancing the biocrude production from pine, which could be upgraded to better fuel using the HYD process.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"24 ","pages":"Article 100735"},"PeriodicalIF":7.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142432959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Electric vehicles (EVs) have gained significant attention in recent years due to their potential to reduce greenhouse gas emissions and improve energy efficiency. An EV’s main source of power is its battery, which plays a crucial role in determining the vehicle’s overall performance and sustainability. The purpose of this paper is to examine the advancements in battery technology associated with EVs and the various charging standards applicable to EVs. Additionally, the most common types of automotive batteries are described and compared. Moreover, the application of artificial intelligence (AI) in EVs has been discussed. Finally, the challenges associated with EV battery development, as well as suggestions for improvement, are discussed. According to the study, Lithium-ion batteries are the most common in EVs due to their high energy density, long lifespan, and cost-effectiveness, despite their temperature sensitivity. Other battery types, like lead-acid and nickel-based, vary in efficiency, but are less commonly used in modern EVs. Solid-state batteries are seen as the future for their higher energy density and faster charging, though they face challenges like flammability. Wireless charging technology, still in development, promises superior convenience and sustainability than traditional methods. AI improves EV performance through enhanced battery management, autonomous driving, vehicle-to-grid communication, etc. Overcoming challenges like battery recycling, metal scarcity, and charging infrastructure will be crucial for the widespread adoption of EVs. This will be supported by government policies and battery technology innovations.
{"title":"Electric vehicles: Battery technologies, charging standards, AI communications, challenges, and future directions","authors":"Mohammed Amer , Jafar Masri , Alya’ Dababat , Uzair Sajjad , Khalid Hamid","doi":"10.1016/j.ecmx.2024.100751","DOIUrl":"10.1016/j.ecmx.2024.100751","url":null,"abstract":"<div><div>Electric vehicles (EVs) have gained significant attention in recent years due to their potential to reduce greenhouse gas emissions and improve energy efficiency. An EV’s main source of power is its battery, which plays a crucial role in determining the vehicle’s overall performance and sustainability. The purpose of this paper is to examine the advancements in battery technology associated with EVs and the various charging standards applicable to EVs. Additionally, the most common types of automotive batteries are described and compared. Moreover, the application of artificial intelligence (AI) in EVs has been discussed. Finally, the challenges associated with EV battery development, as well as suggestions for improvement, are discussed. According to the study, Lithium-ion batteries are the most common in EVs due to their high energy density, long lifespan, and cost-effectiveness, despite their temperature sensitivity. Other battery types, like lead-acid and nickel-based, vary in efficiency, but are less commonly used in modern EVs. Solid-state batteries are seen as the future for their higher energy density and faster charging, though they face challenges like flammability. Wireless charging technology, still in development, promises superior convenience and sustainability than traditional methods. AI improves EV performance through enhanced battery management, autonomous driving, vehicle-to-grid communication, etc. Overcoming challenges like battery recycling, metal scarcity, and charging infrastructure will be crucial for the widespread adoption of EVs. This will be supported by government policies and battery technology innovations.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"24 ","pages":"Article 100751"},"PeriodicalIF":7.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142432963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01DOI: 10.1016/j.ecmx.2024.100742
Muhannad S. Al-Khelaiwi , Talal A. Al-Masaabi , Hany Farag , Shafiqur Rehman
The Kingdom of Saudi Arabia has rich renewable energy resources, specifically wind and solar in addition to geothermal beside massive natural gas reserves. This paper investigates the potential of both green and blue hydrogen production for five selected cities in Saudi Arabia. To accomplish the said objective, a techno-economic model is formulated. Four renewable energy scenarios are evaluated for a total of 1.9 GW installed capacity to reveal the best scenario of Green Hydrogen Production (GHP) in each city. Also, Blue Hydrogen Production (BHP) is investigated for two cases of Steam Methane Reforming (SMR) with different percentages of carbon capture. The two BHP scenarios were compared with a base case scenario of hydrogen production from natural gas without CCS/U (gray hydrogen). The economic analysis for both GHP and BHP is performed by calculating the Levelized Cost of Hydrogen (LCOH) and cash flow. The LCOH for GHP range for all cities ($3.27/kg–$12.17/kg)) with the lowest LCOH is found for NEOM city (50% PV and 50% wind) ($3.27/kg). LCOH for the three SMR cases are $0.534/kg, $0.647/kg, and $0.897/kg for SMR wo CCS/U, SMR 55% CCS/U, and SMR 90% CCS/U respectively.
{"title":"Evaluation of Green and Blue Hydrogen Production Potential in Saudi Arabia","authors":"Muhannad S. Al-Khelaiwi , Talal A. Al-Masaabi , Hany Farag , Shafiqur Rehman","doi":"10.1016/j.ecmx.2024.100742","DOIUrl":"10.1016/j.ecmx.2024.100742","url":null,"abstract":"<div><div>The Kingdom of Saudi Arabia has rich renewable energy resources, specifically wind and solar in addition to geothermal beside massive natural gas reserves. This paper investigates the potential of both green and blue hydrogen production for five selected cities in Saudi Arabia. To accomplish the said objective, a techno-economic model is formulated. Four renewable energy scenarios are evaluated for a total of 1.9 GW installed capacity to reveal the best scenario of Green Hydrogen Production (GHP) in each city. Also, Blue Hydrogen Production (BHP) is investigated for two cases of Steam Methane Reforming (SMR) with different percentages of carbon capture. The two BHP scenarios were compared with a base case scenario of hydrogen production from natural gas without CCS/U (gray hydrogen). The economic analysis for both GHP and BHP is performed by calculating the Levelized Cost of Hydrogen (LCOH) and cash flow. The LCOH for GHP range for all cities ($3.27/kg–$12.17/kg)<sub>)</sub> with the lowest LCOH is found for NEOM city (50% PV and 50% wind) ($3.27/kg). LCOH for the three SMR cases are $0.534/kg, $0.647/kg, and $0.897/kg for SMR wo CCS/U, SMR 55% CCS/U, and SMR 90% CCS/U respectively.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"24 ","pages":"Article 100742"},"PeriodicalIF":7.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号