Pub Date : 2024-11-22DOI: 10.1016/j.apenergy.2024.124826
Qianhui Jiao , Jinghui Wang , Long Cheng , Xuewu Chen , Qing Yu
Encouraging car travelers to switch to public transport is an effective measure to alleviate urban traffic congestion and reduce traffic carbon emissions. This study integrates travel survey data with multi-dimensional individual travel data to focus on incentive strategies. The research identifies key target groups for green travel incentives and quantifies the carbon reduction potential and cost-benefit effectiveness of differentiated incentive strategies for heterogeneous car users. Using Nanjing as a case study, the results show that low-income groups, long-distance commuters, and those with lower car dependency are primary targets users for these incentives. The optimal periods for implementing these strategies are during morning and evening peak commuting times. There is a positive correlation between overall carbon reduction and incentive levels. With a green travel incentive of 0.5 yuan per trip, the target group’s carbon emissions from travel decreased by 27.3%. The highest cost-effectiveness was observed with a 0.1 yuan per trip incentive, resulting in a reduction of approximately 280 yuan per ton of carbon. This study provides crucial insights for designing effective green incentive strategies, enhancing both cost-efficiency and carbon reduction in urban transport.
{"title":"Carbon emission reduction effects of heterogeneous car travelers under green travel incentive strategies","authors":"Qianhui Jiao , Jinghui Wang , Long Cheng , Xuewu Chen , Qing Yu","doi":"10.1016/j.apenergy.2024.124826","DOIUrl":"10.1016/j.apenergy.2024.124826","url":null,"abstract":"<div><div>Encouraging car travelers to switch to public transport is an effective measure to alleviate urban traffic congestion and reduce traffic carbon emissions. This study integrates travel survey data with multi-dimensional individual travel data to focus on incentive strategies. The research identifies key target groups for green travel incentives and quantifies the carbon reduction potential and cost-benefit effectiveness of differentiated incentive strategies for heterogeneous car users. Using Nanjing as a case study, the results show that low-income groups, long-distance commuters, and those with lower car dependency are primary targets users for these incentives. The optimal periods for implementing these strategies are during morning and evening peak commuting times. There is a positive correlation between overall carbon reduction and incentive levels. With a green travel incentive of 0.5 yuan per trip, the target group’s carbon emissions from travel decreased by 27.3%. The highest cost-effectiveness was observed with a 0.1 yuan per trip incentive, resulting in a reduction of approximately 280 yuan per ton of carbon. This study provides crucial insights for designing effective green incentive strategies, enhancing both cost-efficiency and carbon reduction in urban transport.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"379 ","pages":"Article 124826"},"PeriodicalIF":10.1,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142704731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the increased coupling of agriculture and energy, there is a trend to aggregate and manage distributed energy resources in agricultural parks using rural virtual power plants (RVPP). This paper investigates the impact of uncertainties in renewable energy generation and energy usage, as well as the flexibility of energy‑carbon-green certificate (GC) trading, on the planning and operation of RVPP. Firstly, the basic architecture of RVPP is constructed, and a joint trading mechanism for the carbon emission allowance (CEA) and GC is designed. On this basis, a two-stage deterministic optimization model is developed considering capacity configuration in the planning stage and the Stackelberg game in the operation stage of RVPP. Then, several typical scenarios considering the correlation of uncertainties are generated, and the deterministic model is transformed into a distributionally robust optimization (DRO) model in a scenario-driven manner. The confidence intervals of the scenario probability distributions are constrained by a combination of 1-norm and infinity-norm. Finally, the DRO model is decomposed into two problems, solved iteratively using a revised Kriging model and a column-and-constraint generation (C&CG) algorithm. Several cases covering different transaction forms and solution methods are analyzed comparatively to validate the effectiveness of the DRO model. The simulation results indicate that, compared to the energy trading with a fixed price, flexible trading based on the Stackelberg game can reduce the total planning and operating costs by 22.49 %. Compared to the separate trading of GC and CEA, the trading volume of CEA decreases by 44.21 % under the joint trading mechanism, with the increased configuration of renewable energy resources.
{"title":"Scenario-driven distributionally robust optimization model for a rural virtual power plant considering flexible energy-carbon-green certificate trading","authors":"Jinye Cao , Chunlei Xu , Zhuoya Siqin , Miao Yu , Ruisheng Diao","doi":"10.1016/j.apenergy.2024.124904","DOIUrl":"10.1016/j.apenergy.2024.124904","url":null,"abstract":"<div><div>With the increased coupling of agriculture and energy, there is a trend to aggregate and manage distributed energy resources in agricultural parks using rural virtual power plants (RVPP). This paper investigates the impact of uncertainties in renewable energy generation and energy usage, as well as the flexibility of energy‑carbon-green certificate (GC) trading, on the planning and operation of RVPP. Firstly, the basic architecture of RVPP is constructed, and a joint trading mechanism for the carbon emission allowance (CEA) and GC is designed. On this basis, a two-stage deterministic optimization model is developed considering capacity configuration in the planning stage and the Stackelberg game in the operation stage of RVPP. Then, several typical scenarios considering the correlation of uncertainties are generated, and the deterministic model is transformed into a distributionally robust optimization (DRO) model in a scenario-driven manner. The confidence intervals of the scenario probability distributions are constrained by a combination of 1-norm and infinity-norm. Finally, the DRO model is decomposed into two problems, solved iteratively using a revised Kriging model and a column-and-constraint generation (C&CG) algorithm. Several cases covering different transaction forms and solution methods are analyzed comparatively to validate the effectiveness of the DRO model. The simulation results indicate that, compared to the energy trading with a fixed price, flexible trading based on the Stackelberg game can reduce the total planning and operating costs by 22.49 %. Compared to the separate trading of GC and CEA, the trading volume of CEA decreases by 44.21 % under the joint trading mechanism, with the increased configuration of renewable energy resources.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"379 ","pages":"Article 124904"},"PeriodicalIF":10.1,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142704726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-22DOI: 10.1016/j.apenergy.2024.124894
Gang Yu , Xianming Ye , Dunwei Gong , Xiaohua Xia
Shopping mall parking lots are promising and popular sites across nations to be transitioned into charging stations due to the nature of land availability and attractiveness to people. Sufficient charging poles contribute to satisfactory user experience, but excessive charging poles jeopardise the financial feasibility. In this study, an optimal transition planning strategy is proposed that carefully balances the number of charging poles to maximise financial returns while ensuring user convenience. For this purpose, a charging demand model at shopping malls is obtained from historical parking records. A real-time parking bay allocation strategy is obtained according to the charging requests against the available charging poles with the consideration of the maximum demand tariff. To handle the inherent uncertainty of charging demand, we formulate the optimal transition planning problem into a stochastic programming framework. In the case study, we investigate the optimal transition plan for a shopping mall parking lot in the United Kingdom. The optimal results show the transition planning method increases the annual profit by 34% and user satisfaction by 37% compared to the baseline method. The insights for the transition plans that accommodate varying factors including EV penetration, types of charging poles, and charging prices are provided.
{"title":"Stochastic planning for transition from shopping mall parking lots to electric vehicle charging stations","authors":"Gang Yu , Xianming Ye , Dunwei Gong , Xiaohua Xia","doi":"10.1016/j.apenergy.2024.124894","DOIUrl":"10.1016/j.apenergy.2024.124894","url":null,"abstract":"<div><div>Shopping mall parking lots are promising and popular sites across nations to be transitioned into charging stations due to the nature of land availability and attractiveness to people. Sufficient charging poles contribute to satisfactory user experience, but excessive charging poles jeopardise the financial feasibility. In this study, an optimal transition planning strategy is proposed that carefully balances the number of charging poles to maximise financial returns while ensuring user convenience. For this purpose, a charging demand model at shopping malls is obtained from historical parking records. A real-time parking bay allocation strategy is obtained according to the charging requests against the available charging poles with the consideration of the maximum demand tariff. To handle the inherent uncertainty of charging demand, we formulate the optimal transition planning problem into a stochastic programming framework. In the case study, we investigate the optimal transition plan for a shopping mall parking lot in the United Kingdom. The optimal results show the transition planning method increases the annual profit by 34% and user satisfaction by 37% compared to the baseline method. The insights for the transition plans that accommodate varying factors including EV penetration, types of charging poles, and charging prices are provided.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"379 ","pages":"Article 124894"},"PeriodicalIF":10.1,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142704832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-22DOI: 10.1016/j.apenergy.2024.124840
Maurizio Clemente, Mauro Salazar, Theo Hofman
We present a modeling and optimization framework to design powertrains for a family of electric vehicles, focusing on the concurrent sizing of their motors and batteries. Whilst tailoring these component modules to each individual vehicle type can minimize energy consumption, it can result in high production costs due to the variety of component modules to be realized for the family of vehicles, driving the Total Costs of Ownership (TCO) high. Against this backdrop, we explore modularity and standardization strategies whereby we jointly design unique motor and battery modules to be installed in all the vehicles in the family, using a different number of these modules when needed. Such an approach results in higher production volumes of the same component module, entailing significantly lower manufacturing costs due to Economy-of-Scale (EoS) effects, and hence a potentially lower TCO for the family of vehicles. To solve the resulting “one-size-fits-all” problem, we instantiate a nested framework consisting of an inner convex optimization routine which jointly optimizes the modules’ sizes and the powertrain operation of the entire family, for given driving cycles and modules’ multiplicities. Likewise, we devise an outer loop comparing each configuration to identify the minimum-TCO solution with global optimality guarantees. Finally, we showcase our framework on a case study for the Tesla vehicle family in a benchmark design problem, considering the Model S, Model 3, Model X, and Model Y. Our results show that, compared to an individually tailored design, the application of our concurrent design optimization framework achieves a significant reduction of the production costs for a minimal increase in operational costs, ultimately lowering the family TCO in the benchmark design problem by 3.5%. Moreover, our concurrent design optimization methodology can reduce the TCO by up to 17% for the market conditions considered in our sensitivity study.
我们提出了一个建模和优化框架，用于设计电动汽车系列的动力系统，重点是同时确定电机和电池的尺寸。虽然为每种车型量身定制这些组件模块可以最大限度地降低能耗，但由于要为汽车家族实现各种组件模块，这可能会导致生产成本居高不下，从而使总体拥有成本（TCO）居高不下。在此背景下，我们探索了模块化和标准化战略，即共同设计独特的电机和电池模块，安装在系列中的所有车辆上，并在需要时使用不同数量的这些模块。这种方法可以提高相同组件模块的产量，从而在规模经济效应（EoS）的作用下大幅降低制造成本，进而降低汽车家族的总体拥有成本。为了解决由此产生的 "一刀切 "问题，我们建立了一个嵌套框架，该框架由一个内凸优化程序组成，在给定的驾驶周期和模块倍率条件下，共同优化模块尺寸和整个系列的动力总成运行。同样，我们还设计了一个外循环，对每种配置进行比较，以确定具有全局最优性保证的最小总拥有成本解决方案。最后，我们在特斯拉汽车家族的基准设计问题案例研究中展示了我们的框架，考虑了 Model S、Model 3、Model X 和 Model Y。我们的结果表明，与单独定制的设计相比，应用我们的并发设计优化框架能以最小的运营成本增幅显著降低生产成本，最终将基准设计问题中的家族总拥有成本降低了 3.5%。此外，在敏感性研究中考虑的市场条件下，我们的并行设计优化方法最多可将总拥有成本降低 17%。
{"title":"Concurrent design optimization of powertrain component modules in a family of electric vehicles","authors":"Maurizio Clemente, Mauro Salazar, Theo Hofman","doi":"10.1016/j.apenergy.2024.124840","DOIUrl":"10.1016/j.apenergy.2024.124840","url":null,"abstract":"<div><div>We present a modeling and optimization framework to design powertrains for a family of electric vehicles, focusing on the concurrent sizing of their motors and batteries. Whilst tailoring these component modules to each individual vehicle type can minimize energy consumption, it can result in high production costs due to the variety of component modules to be realized for the family of vehicles, driving the Total Costs of Ownership (TCO) high. Against this backdrop, we explore modularity and standardization strategies whereby we jointly design unique motor and battery modules to be installed in all the vehicles in the family, using a different number of these modules when needed. Such an approach results in higher production volumes of the same component module, entailing significantly lower manufacturing costs due to Economy-of-Scale (EoS) effects, and hence a potentially lower TCO for the family of vehicles. To solve the resulting “one-size-fits-all” problem, we instantiate a nested framework consisting of an inner convex optimization routine which jointly optimizes the modules’ sizes and the powertrain operation of the entire family, for given driving cycles and modules’ multiplicities. Likewise, we devise an outer loop comparing each configuration to identify the minimum-TCO solution with global optimality guarantees. Finally, we showcase our framework on a case study for the Tesla vehicle family in a benchmark design problem, considering the Model S, Model 3, Model X, and Model Y. Our results show that, compared to an individually tailored design, the application of our concurrent design optimization framework achieves a significant reduction of the production costs for a minimal increase in operational costs, ultimately lowering the family TCO in the benchmark design problem by 3.5%. Moreover, our concurrent design optimization methodology can reduce the TCO by up to 17% for the market conditions considered in our sensitivity study.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"379 ","pages":"Article 124840"},"PeriodicalIF":10.1,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142704725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-22DOI: 10.1016/j.apenergy.2024.124902
Cláudio A.C. Cambambi, Luciane N. Canha, Maurício Sperandio, Camilo Rangel, Isabel P. Milani
This paper describes a hierarchical energy management system for multiple geolocated microgrids, with the aim of minimizing operational costs and maximizing individual benefits. To achieve this goal, a coalition formation algorithm is developed to optimize energy exchanges between geolocated microgrids, leading to a significant reduction in costs. At the local level (first layer), optimization is performed using mixed-integer linear programming, while at the central level, the optimization is carried out through the coalition formation algorithm. The formation of coalitions among geolocated microgrids has demonstrated substantial benefits. For instance, coalitions showed the highest percentage reductions in losses (52.63% to 71.53%) in the cooperative state compared to the non-cooperative state, indicating significant cost savings. In contrast, lower percentage reductions in losses (12.08% to 16.10%) were observed, yet they still benefited from reduced operational costs. Throughout the day, the cooperative method consistently proved to be more effective than the non-cooperative method. The effectiveness of coalitions in reducing losses and operational costs is demonstrated, emphasizing the importance of flexible approaches in addressing challenges such as geolocation and variable weather conditions. This study contributes to the advancement of distributed energy systems, supporting the transition to more sustainable and resilient systems.
{"title":"Energy exchange optimization among multiple geolocated microgrids: A coalition formation approach for cost reduction","authors":"Cláudio A.C. Cambambi, Luciane N. Canha, Maurício Sperandio, Camilo Rangel, Isabel P. Milani","doi":"10.1016/j.apenergy.2024.124902","DOIUrl":"10.1016/j.apenergy.2024.124902","url":null,"abstract":"<div><div>This paper describes a hierarchical energy management system for multiple geolocated microgrids, with the aim of minimizing operational costs and maximizing individual benefits. To achieve this goal, a coalition formation algorithm is developed to optimize energy exchanges between geolocated microgrids, leading to a significant reduction in costs. At the local level (first layer), optimization is performed using mixed-integer linear programming, while at the central level, the optimization is carried out through the coalition formation algorithm. The formation of coalitions among geolocated microgrids has demonstrated substantial benefits. For instance, coalitions showed the highest percentage reductions in losses (52.63% to 71.53%) in the cooperative state compared to the non-cooperative state, indicating significant cost savings. In contrast, lower percentage reductions in losses (12.08% to 16.10%) were observed, yet they still benefited from reduced operational costs. Throughout the day, the cooperative method consistently proved to be more effective than the non-cooperative method. The effectiveness of coalitions in reducing losses and operational costs is demonstrated, emphasizing the importance of flexible approaches in addressing challenges such as geolocation and variable weather conditions. This study contributes to the advancement of distributed energy systems, supporting the transition to more sustainable and resilient systems.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"379 ","pages":"Article 124902"},"PeriodicalIF":10.1,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142704833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-22DOI: 10.1016/j.apenergy.2024.124949
Kannie Winston Kuttin , Ahmed M. Salem , Lu Ding , Guangsuo Yu
Thermally converting sewage sludge (SS) to harness its energy potential brings challenges like high ash content, which can cause system instability. However, co-gasifying SS with carbon-rich materials has shown to be more advantageous. This study analyses a two-dimensional Eulerian CFD numerical model for a CO2 and steam cogasification of SS and palm kernel shell in a downdraft gasifier by solving the governing equations of mass phases, turbulence, energy, and momentum on a high-resolution mesh model. The devolatilization phase is defined by comprehensive solid carbon pyrolysis kinetic mechanisms and secondary gas reaction mechanisms, whilst the gasification and combustion processes are governed by applying detailed heterogeneous and homogeneous rate-controlled reactions. An experimental assessment with temperature of 1000 °C; ER of 0.28; fuel feed rate of 0.00061 kgs−1; at atmospheric pressure was conducted to validate the current model considering the temperature distribution and gas composition. The validated model is further used to evaluate the effect of mixing ratios, steam-to-fuel ratio (S/F), carbon dioxide-fuel ratio (CO2/F), and gasification temperature on syngas composition, lower heating value, hot gas efficiency, gas yield, H2/CO and synergistic coefficient. The predicted results agree well with the experimental data with the maximum deviational error of 9.52 %. The highest H2 production was recorded during steam gasification, whilst CO was favored by CO2 gasification. Synergistic analysis presented the highest synergy coefficient for the SS mixing ratio of 25 % at 1.91 and 50 % at 1.93 for steam and CO2 gasification respectively.
对污水污泥（SS）进行热转化以利用其能源潜力会带来一些挑战，如高灰分会导致系统不稳定。然而，将 SS 与富碳材料进行联合气化则更具优势。本研究通过在高分辨率网格模型上求解质相、湍流、能量和动量的控制方程，分析了在下吹气化炉中对 SS 和棕榈仁壳进行二氧化碳和蒸汽气化的二维欧拉 CFD 数值模型。固体碳热解动力学机理和二次气体反应机理对脱胶阶段进行了定义，而气化和燃烧过程则由详细的异质和均质速率控制反应来控制。在温度为 1000 °C、ER 为 0.28、燃料进料率为 0.00061 kgs-1、常压条件下进行了实验评估，以验证当前模型的温度分布和气体成分。验证后的模型进一步用于评估混合比、蒸汽与燃料比 (S/F)、二氧化碳与燃料比 (CO2/F) 和气化温度对合成气成分、较低热值、热气效率、产气量、H2/CO 和协同系数的影响。预测结果与实验数据十分吻合，最大偏差为 9.52%。蒸汽气化法的 H2 产量最高，而 CO2 气化法的 CO 产量较高。协同分析表明，在蒸汽气化和二氧化碳气化过程中，当 SS 混合比为 25% 时，协同系数最高，分别为 1.91 和 1.93。
{"title":"Parametric evaluation of carbon dioxide and steam co-gasification of sewage sludge and palm kernel shell in a downdraft fixed bed reactor: Computational Fluid Dynamics (CFD) approach","authors":"Kannie Winston Kuttin , Ahmed M. Salem , Lu Ding , Guangsuo Yu","doi":"10.1016/j.apenergy.2024.124949","DOIUrl":"10.1016/j.apenergy.2024.124949","url":null,"abstract":"<div><div>Thermally converting sewage sludge (SS) to harness its energy potential brings challenges like high ash content, which can cause system instability. However, co-gasifying SS with carbon-rich materials has shown to be more advantageous. This study analyses a two-dimensional Eulerian CFD numerical model for a CO<sub>2</sub> and steam cogasification of SS and palm kernel shell in a downdraft gasifier by solving the governing equations of mass phases, turbulence, energy, and momentum on a high-resolution mesh model. The devolatilization phase is defined by comprehensive solid carbon pyrolysis kinetic mechanisms and secondary gas reaction mechanisms, whilst the gasification and combustion processes are governed by applying detailed heterogeneous and homogeneous rate-controlled reactions. An experimental assessment with temperature of 1000 °C; ER of 0.28; fuel feed rate of 0.00061 kgs<sup>−1</sup>; at atmospheric pressure was conducted to validate the current model considering the temperature distribution and gas composition. The validated model is further used to evaluate the effect of mixing ratios, steam-to-fuel ratio (S/F), carbon dioxide-fuel ratio (CO<sub>2</sub>/F), and gasification temperature on syngas composition, lower heating value, hot gas efficiency, gas yield, H<sub>2</sub>/CO and synergistic coefficient. The predicted results agree well with the experimental data with the maximum deviational error of 9.52 %. The highest H<sub>2</sub> production was recorded during steam gasification, whilst CO was favored by CO<sub>2</sub> gasification. Synergistic analysis presented the highest synergy coefficient for the SS mixing ratio of 25 % at 1.91 and 50 % at 1.93 for steam and CO<sub>2</sub> gasification respectively.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"379 ","pages":"Article 124949"},"PeriodicalIF":10.1,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142704723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-21DOI: 10.1016/j.apenergy.2024.124924
Elaheh Sadeh , Abdolreza Farhadian , Mina Maddah , Matvei E. Semenov , Evgeniy R. Son , Atousa Heydari , Ulukbek Zh. Mirzakimov , Lenar R. Valiullin , Mikhail A. Varfolomeev
The development of efficient, non-foaming promoters is essential for advancing the industrial applications of solidified gas hydrates in carbon capture, natural gas storage, and transportation. In this study, a novel surfactant, containing sulfonate, amide, and carboxyl groups (SSAC), was introduced as a promoter for methane hydrate formation. SSAC was synthesized by integrating the chemistries of amino acids and sodium dodecyl sulfate (SDS), distinguishing it from existing promoters. High-pressure autoclave experiments demonstrated that SSAC significantly enhanced the kinetics of methane hydrate formation, at a low concentration of 5 ppm, achieving a maximum water-to-hydrate conversion of 85.2 %, equivalent to a storage capacity of 163.5 v/v in deionized water. Increasing the SSAC concentration to 500 ppm resulted in an impressive conversion rate of 94.6 % and a storage capacity of 181.6 v/v. Methane recovery was accomplished without foaming within 15 min during hydrate dissociation at room temperature, addressing a critical challenge in current hydrate-based storage systems. Molecular dynamics simulations further revealed that SSAC molecules act as collectors for methane molecules in solution, thereby enhancing the rate of hydrate growth and increasing the number of hydrate cavities. Notably, SSAC exhibited a biodegradation level of 41 % after 28 days, indicating its potential for natural degradation and environmental compatibility. This combination of low concentration efficiency, foam-free formation, environmental sustainability, and enhanced methane collection is unprecedented in the current literature, highlighting the innovative nature of this work. These findings suggest that the integration of amino acid structures with anionic surfactants offers a promising strategy for designing effective promoters, with significant implications for energy storage, seawater desalination, and carbon capture technologies.
{"title":"High storage capacity and rapid methane hydrate formation using low concentrations of a new surfactant: A mimic of SDS and amino acid scaffold","authors":"Elaheh Sadeh , Abdolreza Farhadian , Mina Maddah , Matvei E. Semenov , Evgeniy R. Son , Atousa Heydari , Ulukbek Zh. Mirzakimov , Lenar R. Valiullin , Mikhail A. Varfolomeev","doi":"10.1016/j.apenergy.2024.124924","DOIUrl":"10.1016/j.apenergy.2024.124924","url":null,"abstract":"<div><div>The development of efficient, non-foaming promoters is essential for advancing the industrial applications of solidified gas hydrates in carbon capture, natural gas storage, and transportation. In this study, a novel surfactant, containing sulfonate, amide, and carboxyl groups (SSAC), was introduced as a promoter for methane hydrate formation. SSAC was synthesized by integrating the chemistries of amino acids and sodium dodecyl sulfate (SDS), distinguishing it from existing promoters. High-pressure autoclave experiments demonstrated that SSAC significantly enhanced the kinetics of methane hydrate formation, at a low concentration of 5 ppm, achieving a maximum water-to-hydrate conversion of 85.2 %, equivalent to a storage capacity of 163.5 <em>v</em>/v in deionized water. Increasing the SSAC concentration to 500 ppm resulted in an impressive conversion rate of 94.6 % and a storage capacity of 181.6 v/v. Methane recovery was accomplished without foaming within 15 min during hydrate dissociation at room temperature, addressing a critical challenge in current hydrate-based storage systems. Molecular dynamics simulations further revealed that SSAC molecules act as collectors for methane molecules in solution, thereby enhancing the rate of hydrate growth and increasing the number of hydrate cavities. Notably, SSAC exhibited a biodegradation level of 41 % after 28 days, indicating its potential for natural degradation and environmental compatibility. This combination of low concentration efficiency, foam-free formation, environmental sustainability, and enhanced methane collection is unprecedented in the current literature, highlighting the innovative nature of this work. These findings suggest that the integration of amino acid structures with anionic surfactants offers a promising strategy for designing effective promoters, with significant implications for energy storage, seawater desalination, and carbon capture technologies.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"379 ","pages":"Article 124924"},"PeriodicalIF":10.1,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142703917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-21DOI: 10.1016/j.apenergy.2024.124898
Chao Song , Jinbo Che , Fengnian Wang , Rui Wang , Yinshi Li
Conventional solar thermochemical heat collector with direct solar-heating usually faces an issue of aperture being contaminated and the difficulty of real-time particle velocity control. Here, a three-dimensional multi-physics numerical solver coupling with optical and thermal stress sub-models is developed towards heat storage mechanization of calcium looping, considering discrete particle flow, continuous gas flow, solar radiation, temperature field, particle collision force and chemical reaction. Based on the CFD-DEM method, the particle velocity and temperature distribution in the moving bed collector present non-uniformity with a parabolic profile. Numerical simulation results show that the energy carriers can reach the high temperature of 1350 K with a calcination rate of 1.1 × 10−8 kmol s−1 under the incident power of 6.68 kW, exhibiting an efficient performance. Thermal stress sub-model of energy carriers, implemented by coupling the in-house code with CFD-DEM, reveals that high temperatures lead to a better conversion rate of CaL but with a higher risk of thermal fragmentation. A new wedge-shaped structure of redistributor is further proposed to effectively alleviate the non-uniformity of the particles flow and temperature distribution. The effect of solar energy input flux, particles absorptivity and emissivity are systematically investigated, laying a solid foundation for the further research on industrial amplification processes.
传统的太阳能直接加热热化学集热器通常面临着孔径被污染和颗粒速度难以实时控制的问题。本文针对钙循环蓄热机械化问题，考虑离散粒子流、连续气体流、太阳辐射、温度场、粒子碰撞力和化学反应等因素，建立了光学和热应力子模型耦合的三维多物理场数值求解器。基于 CFD-DEM 方法，移动床收集器中的颗粒速度和温度分布呈现出抛物线轮廓的非均匀性。数值模拟结果表明，在入射功率为 6.68 kW 的情况下，能量载体可以达到 1350 K 的高温，煅烧速率为 1.1 × 10-8 kmol s-1，表现出高效的性能。通过将内部代码与 CFD-DEM 相结合而实现的能量载体热应力子模型显示，高温可提高 CaL 的转化率，但热碎裂的风险较高。此外，还提出了一种新的楔形再分布器结构，以有效缓解颗粒流动和温度分布的不均匀性。系统研究了太阳能输入通量、粒子吸收率和发射率的影响，为进一步研究工业放大过程奠定了坚实的基础。
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Pub Date : 2024-11-21DOI: 10.1016/j.apenergy.2024.124922
Dipti Gupta , Minal Pathak
In line with global mitigation ambition and the domestic target of achieving net zero emissions by 2070, India's industrial sector is expected to undergo a major transition. This transition is not trivial given the rapid growth of industrial output, dependence on fossil fuels, high emission intensity, and complicated emission abatement processes. In this paper, we provide a whole systems analysis of the manufacturing industries- iron & steel, aluminium, cement, chemical and petrochemical, textile, residual- for achieving net zero by 2070. The methodology combines the qualitative inputs from stakeholders with the energy-economy modelling using IMACLIM-IND and AIM/Enduse models. We develop four scenarios: Business-As-Usual (BAU), Development First (DFS), Carbon Neutral (CNT) and Synchronous (SYNCH). For each of these scenarios, we assess impacts of the structural transformation on sustainable development mainly through impacts on economy (gross value added and material imports), environment (material resource savings), and investment needs. The SYNCH scenario achieves 63 % emission reduction and requires an investment of 1.7 trillion USD by 2050 compared to BAU. The key policy insight is that new investments should go towards decarbonizing electricity, recycling infrastructure, and Carbon Capture and Storage. Clear standards and regulations for emission reporting by the production firms should be stipulated by the government.
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Pub Date : 2024-11-21DOI: 10.1016/j.apenergy.2024.124895
Øyvind Sommer Klyve , Ville Olkkonen , Magnus Moe Nygård , David Lingfors , Erik Stensrud Marstein , Oskar Lindberg
The concept of hybrid power plants (HPPs), wherein co-located solar photovoltaic (PV) and wind assets share a common point of interconnection (POI) with the grid, is gaining traction. The wind and solar resources can be anti-correlated, and this opens for oversizing the capacity of these HPPs relative to their nominal POI capacity while ensuring low curtailment losses. Thus, retrofitting existing wind power plants into PV-wind HPPs can be a promising strategy in regions where access to the grid is a barrier to capacity expansion. However, it is not clear how the resource availability and anti-correlation of the solar and wind resources at a given location impact the techno-economic feasibility for retrofitting an existing wind power plant with PV capacity.
In this study, 128 existing wind power plants in Norway and Sweden were assessed for retrofitting with PV using a techno-economic model, measured wind power and modeled PV power generation data. Multiple linear regression (MLR) analysis was applied to the resulting cost-optimal HPPs in order to determine which of a site’s resource characteristics that are influencing the feasibility of such retrofitting, and to what extent. The results suggests that the top three key characteristics sorted in order of decreasing importance are: (i) high mean PV capacity factors, (ii) low mean wind capacity factors and (iii) strong anti-correlation between the hourly PV and wind power generation. The results thus demonstrate that developers aiming to retrofit wind power plants with PV capacity should target those located in areas with high solar irradiance and performing badly, i.e., with low wind capacity factors, rather than prioritizing wind power plants at sites with strong anti-correlation between the PV and wind generation. Finally, it is demonstrated how the analysis framework can be used as a screening tool, i.e., as a means of predicting the techno-economic potential for PV retrofitting, also for wind power plants where power generation time series are unavailable.
混合电厂（HPPs）的概念正在受到越来越多的关注，在混合电厂中，太阳能光伏（PV）和风能资产共用一个并网点（POI）。风能和太阳能资源可以是反相关的，这就为相对于其额定 POI 容量而言的超大容量风力发电厂提供了机会，同时还能确保较低的削减损失。因此，将现有的风力发电厂改造成光伏风力发电厂，在电网接入成为发电量扩张障碍的地区，是一项很有前景的战略。在本研究中，利用技术经济模型、测量的风力发电数据和模拟的光伏发电数据，对挪威和瑞典的 128 个现有风力发电厂进行了光伏改造评估。多元线性回归（MLR）分析被应用于由此得出的成本最优的风力发电厂，以确定哪些场地的资源特征会影响此类改造的可行性，以及影响程度如何。结果表明，按重要性递减顺序排列的前三个关键特征是(i) 平均光伏发电能力系数高；(ii) 平均风力发电能力系数低；(iii) 每小时光伏发电量和风力发电量之间的反相关性强。因此，研究结果表明，开发商在对风力发电厂进行光伏发电改造时，应选择位于太阳辐照度高且性能较差地区的风力发电厂，即风力发电容量系数较低的风力发电厂，而不是优先选择光伏发电量与风力发电量反相关性较强的风力发电厂。最后，演示了如何将分析框架用作筛选工具，即预测光伏改造的技术经济潜力，也可用于发电时间序列不可用的风力发电厂。
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