Pub Date : 2024-11-15DOI: 10.1016/j.enconman.2024.119217
Paula Rojas, Nicolás Alegría, Mario Toledo
Climate change has made clear the need to decarbonize the global energy matrix, and green hydrogen has emerged as a promising alternative fuel. In this framework, this work investigates the green hydrogen production by means of a two-stage thermochemical water-splitting reactor heated by both a parabolic dish receiver and a photovoltaic heater. A mathematical model is proposed to simulate reduction–oxidation process for the solar-powered reactor composed of a porous cerium oxide medium. Experimental and numerical thermal profiles show good agreement, with a high temperature in the reduction stage (>1100 K) and a lower temperature in the oxidation stage (860–715 K). Green hydrogen productions show maximum values close to 100 ppm and 2000 , for experimental and numerical tests, respectively. It is concluded that the photovoltaic heater is more relevant than the solar concentration heater, and that green hydrogen production could be improved by allowing longer residence times for the reduction–oxidation stages.
{"title":"Numerical and experimental investigation of a two-stage thermochemical water-splitting reactor based on a cerium oxide reduction–oxidation cycle","authors":"Paula Rojas, Nicolás Alegría, Mario Toledo","doi":"10.1016/j.enconman.2024.119217","DOIUrl":"10.1016/j.enconman.2024.119217","url":null,"abstract":"<div><div>Climate change has made clear the need to decarbonize the global energy matrix, and green hydrogen has emerged as a promising alternative fuel. In this framework, this work investigates the green hydrogen production by means of a two-stage thermochemical water-splitting reactor heated by both a parabolic dish receiver and a photovoltaic heater. A mathematical model is proposed to simulate reduction–oxidation process for the solar-powered reactor composed of a porous cerium oxide medium. Experimental and numerical thermal profiles show good agreement, with a high temperature in the reduction stage (>1100 K) and a lower temperature in the oxidation stage (860–715 K). Green hydrogen productions show maximum values close to 100 ppm and 2000 <span><math><mrow><mi>μ</mi><mi>m</mi><mi>o</mi><msub><mi>l</mi><mrow><msub><mi>H</mi><mn>2</mn></msub><mi>O</mi></mrow></msub><mo>/</mo><msub><mi>g</mi><mrow><mi>C</mi><mi>e</mi><msub><mi>O</mi><mn>2</mn></msub></mrow></msub></mrow></math></span>, for experimental and numerical tests, respectively. It is concluded that the photovoltaic heater is more relevant than the solar concentration heater, and that green hydrogen production could be improved by allowing longer residence times for the reduction–oxidation stages.</div></div>","PeriodicalId":11664,"journal":{"name":"Energy Conversion and Management","volume":"323 ","pages":"Article 119217"},"PeriodicalIF":9.9,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142637525","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 : 2024-11-15DOI: 10.1016/j.enconman.2024.119216
Pedro Chévez
In the coming years, a major challenge for developing countries will be gaining a deep understanding of their residential energy consumption. This knowledge is crucial for designing targeted energy policies, as accurate insights can guide subsidy allocation, manage consumption, reduce dependence on imports, and address energy shortages. While various methods exist for disaggregating consumption in this sector, countries should prioritize those that are both straightforward for their staff to implement and affordable. This work proposes that a universal, open, and cost-effective bottom-up engineering model, based on a synthetic energy questionnaire and methods for estimating missing variables, can accurately estimate residential energy consumption for a country/region, particularly in those lacking detailed statistical data. This model was applied to an “equipment dataset” from the 2017/2018 Argentine National Household Expenditure Survey and validated on both monthly and annual basis, without the need for individual data collection. It enables the characterization of energy consumption disaggregated by province, by user income segments, by energy sources, by end uses and by month. The case study’s main findings reveal significant energy inequalities among Argentine households, with higher-income households consuming between 39.35% and 90.71% more energy than lower-income households. This work highlights the effectiveness of bottom-up sample models when paired with appropriate methods for estimating uncollected data. A key innovation lies in the model’s open nature, which was designed for universal applicability across climate variables, allowing for easy replication in other studies.
{"title":"An open and cost-effective bottom-up engineering model for comprehensive disaggregation of residential energy consumption in developing countries","authors":"Pedro Chévez","doi":"10.1016/j.enconman.2024.119216","DOIUrl":"10.1016/j.enconman.2024.119216","url":null,"abstract":"<div><div>In the coming years, a major challenge for developing countries will be gaining a deep understanding of their residential energy consumption. This knowledge is crucial for designing targeted energy policies, as accurate insights can guide subsidy allocation, manage consumption, reduce dependence on imports, and address energy shortages. While various methods exist for disaggregating consumption in this sector, countries should prioritize those that are both straightforward for their staff to implement and affordable. This work proposes that a universal, open, and cost-effective bottom-up engineering model, based on a synthetic energy questionnaire and methods for estimating missing variables, can accurately estimate residential energy consumption for a country/region, particularly in those lacking detailed statistical data. This model was applied to an “equipment dataset” from the 2017/2018 Argentine <em>National Household Expenditure Survey</em> and validated on both monthly and annual basis, without the need for individual data collection. It enables the characterization of energy consumption disaggregated by province, by user income segments, by energy sources, by end uses and by month. The case study’s main findings reveal significant energy inequalities among Argentine households, with higher-income households consuming between 39.35% and 90.71% more energy than lower-income households. This work highlights the effectiveness of bottom-up sample models when paired with appropriate methods for estimating uncollected data. A key innovation lies in the model’s open nature, which was designed for universal applicability across climate variables, allowing for easy replication in other studies.</div></div>","PeriodicalId":11664,"journal":{"name":"Energy Conversion and Management","volume":"323 ","pages":"Article 119216"},"PeriodicalIF":9.9,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142637527","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 : 2024-11-14DOI: 10.1016/j.enconman.2024.119219
Jianhua Zhu , Yaoyao He
Photovoltaic (PV) power probabilistic forecasting that provides decision makers with probabilistic information and ranges of PV power generation is critical to the power system. Existing studies have demonstrated that QR-based nonlinear models can generate probability distributions directly from historical data. However, the accuracy of these methods may be degraded when confronting with PV power at high latitude meteorological factors and they inherently have flaws in the model structure and loss function. This paper proposes a novel approach called monotonic quantile convolutional neural network-multi-layer nondominated fast sort genetic algorithm II (MQCNN-MLNSGAII) for solving these challenges. MQCNN first uses the convolutional structure to extract the valid deep features from the high latitude factor, and then designs a monotonic quantile structure to output monotonically increasing probability distributions at once. Considering the high impact of the probability distribution width on the quality of the forecasting, we design two loss functions, average quantile loss (AQS) and quantile distribution average width (QDAW), based on multi-objective optimization (MOO) to balance the reliability and width. Finally, a novel multi-objective evolutionary algorithm (MOEA), MLNSGAII, is proposed for training MQCNN. It develops a multi-layer mechanism based on global and historical information to assist the algorithm in generating diverse offspring and improve the performance in convergence and diversity. Compared to the benchmark models, the proposed model achieves significant strengths in the real Australian dataset.
为决策者提供光伏发电概率信息和范围的光伏发电概率预测对电力系统至关重要。现有研究表明，基于 QR 的非线性模型可以直接从历史数据生成概率分布。然而，当面对高纬度气象因素下的光伏发电时，这些方法的准确性可能会下降，而且它们在模型结构和损失函数方面存在固有缺陷。本文提出了一种名为单调量子卷积神经网络-多层非支配快速排序遗传算法 II（MQCNN-MLNSGAII）的新方法来解决这些难题。MQCNN 首先利用卷积结构从高纬度因子中提取有效的深度特征，然后设计单调量子结构，一次性输出单调递增的概率分布。考虑到概率分布宽度对预测质量的影响较大，我们基于多目标优化（MOO）设计了两个损失函数，即平均量子损失（AQS）和量子分布平均宽度（QDAW），以平衡可靠性和宽度。最后，为训练 MQCNN 提出了一种新型多目标进化算法（MOEA），即 MLNSGAII。它开发了一种基于全局和历史信息的多层机制，以帮助算法生成多样化的后代，并提高收敛性和多样性方面的性能。与基准模型相比，所提出的模型在实际的澳大利亚数据集中取得了显著的优势。
{"title":"A novel photovoltaic power probabilistic forecasting model based on monotonic quantile convolutional neural network and multi-objective optimization","authors":"Jianhua Zhu , Yaoyao He","doi":"10.1016/j.enconman.2024.119219","DOIUrl":"10.1016/j.enconman.2024.119219","url":null,"abstract":"<div><div>Photovoltaic (PV) power probabilistic forecasting that provides decision makers with probabilistic information and ranges of PV power generation is critical to the power system. Existing studies have demonstrated that QR-based nonlinear models can generate probability distributions directly from historical data. However, the accuracy of these methods may be degraded when confronting with PV power at high latitude meteorological factors and they inherently have flaws in the model structure and loss function. This paper proposes a novel approach called monotonic quantile convolutional neural network-multi-layer nondominated fast sort genetic algorithm II (MQCNN-MLNSGAII) for solving these challenges. MQCNN first uses the convolutional structure to extract the valid deep features from the high latitude factor, and then designs a monotonic quantile structure to output monotonically increasing probability distributions at once. Considering the high impact of the probability distribution width on the quality of the forecasting, we design two loss functions, average quantile loss (AQS) and quantile distribution average width (QDAW), based on multi-objective optimization (MOO) to balance the reliability and width. Finally, a novel multi-objective evolutionary algorithm (MOEA), MLNSGAII, is proposed for training MQCNN. It develops a multi-layer mechanism based on global and historical information to assist the algorithm in generating diverse offspring and improve the performance in convergence and diversity. Compared to the benchmark models, the proposed model achieves significant strengths in the real Australian dataset.</div></div>","PeriodicalId":11664,"journal":{"name":"Energy Conversion and Management","volume":"323 ","pages":"Article 119219"},"PeriodicalIF":9.9,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655895","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 : 2024-11-14DOI: 10.1016/j.enconman.2024.119225
Elisa da Silva Barreto , Yasmim Arantes da Fonseca , Oscar Fernando Herrera Adarme , Débora Faria Silva , Rogélio Lopes Brandão , Bruno Eduardo Lobo Baêta , Valéria Monteze Guimarães , Leandro Vinícius Alves Gurgel
This study presents the upscaling of soda pretreatment of sugarcane bagasse (SB) using a new redox mediator (2-hydroxynaphthalene-1,4-dione) obtained from renewable resources, which does not affect enzymatic hydrolysis and fermentation. Upscaling was performed from a 0.5 L batch static stainless steel reactor to a 20 L pulp digester with forced liquor circulation, analogous to digestors used in the pulp and paper industry. Enzymatic hydrolysis of the pretreated material was optimized using the fed-batch method and was then carried out on a larger scale. The fed-batch method, combined with addition of 1 % (v v−1) Tween 80, enabled the solids load to be increased from 10 % to 15 % (w v−1), with an enzyme load of only 3.00 FPU g−1. This led to a maximum total reducing sugars concentration of ∼142 g L−1 after 72 h of hydrolysis. Co-fermentation of C5 and C6 sugar-rich hydrolysate by a consortium of CERLEV 47 (Saccharomyces cerevisiae) and CERLEV 1015 (Pichia guilliermondii) led to a maximum 2G ethanol production of 61.3 g L−1 (308 L ethanol per ton of SB). Mass and energy balances demonstrated that the combustion of black liquor, a byproduct of the soda pretreatment, could satisfy the energy demands of the pretreatment, enzymatic hydrolysis, and fermentation, with an energy of 21.11 MJ using the surplus SB (80 %) from 1G ethanol production. This finding indicated that the developed process was robust and had the potential to enhance total 2G ethanol production. This study supports the feasibility of an integrated 1G/2G biorefinery by improving energy efficiency, economic viability, and environmental sustainability.
{"title":"Optimization of 2G ethanol production from sugarcane bagasse: Upscaling of soda pretreatment with redox mediator followed by fed-batch enzymatic hydrolysis and co-fermentation","authors":"Elisa da Silva Barreto , Yasmim Arantes da Fonseca , Oscar Fernando Herrera Adarme , Débora Faria Silva , Rogélio Lopes Brandão , Bruno Eduardo Lobo Baêta , Valéria Monteze Guimarães , Leandro Vinícius Alves Gurgel","doi":"10.1016/j.enconman.2024.119225","DOIUrl":"10.1016/j.enconman.2024.119225","url":null,"abstract":"<div><div>This study presents the upscaling of soda pretreatment of sugarcane bagasse (SB) using a new redox mediator (2-hydroxynaphthalene-1,4-dione) obtained from renewable resources, which does not affect enzymatic hydrolysis and fermentation. Upscaling was performed from a 0.5 L batch static stainless steel reactor to a 20 L pulp digester with forced liquor circulation, analogous to digestors used in the pulp and paper industry. Enzymatic hydrolysis of the pretreated material was optimized using the fed-batch method and was then carried out on a larger scale. The fed-batch method, combined with addition of 1 % (v v<sup>−1</sup>) Tween 80, enabled the solids load to be increased from 10 % to 15 % (w v<sup>−1</sup>), with an enzyme load of only 3.00 FPU g<sup>−1</sup>. This led to a maximum total reducing sugars concentration of ∼142 g L<sup>−1</sup> after 72 h of hydrolysis. Co-fermentation of C5 and C6 sugar-rich hydrolysate by a consortium of CERLEV 47 (<em>Saccharomyces cerevisiae</em>) and CERLEV 1015 (<em>Pichia guilliermondii</em>) led to a maximum 2G ethanol production of 61.3 g L<sup>−1</sup> (308 L ethanol per ton of SB). Mass and energy balances demonstrated that the combustion of black liquor, a byproduct of the soda pretreatment, could satisfy the energy demands of the pretreatment, enzymatic hydrolysis, and fermentation, with an energy of 21.11 MJ using the surplus SB (80 %) from 1G ethanol production. This finding indicated that the developed process was robust and had the potential to enhance total 2G ethanol production. This study supports the feasibility of an integrated 1G/2G biorefinery by improving energy efficiency, economic viability, and environmental sustainability.</div></div>","PeriodicalId":11664,"journal":{"name":"Energy Conversion and Management","volume":"323 ","pages":"Article 119225"},"PeriodicalIF":9.9,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655894","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}
Accurately forecasting photovoltaic power generation is essential for the efficient integration of renewable energy into power grids. This paper presents a novel, high-accuracy framework for short-term photovoltaic productivity forecasting, tailored to the climatic conditions of the Algerian region of El-Oued. The framework automatically adapts the neural network forecast using a nature-inspired algorithm, eliminating the need for manual adjustments. It first addresses the complex, non-stationary nature of photovoltaic generation by incorporating a time-varying filter based on empirical mode decomposition, which decomposes the original photovoltaic data into multiple low-frequency components. Particle swarm optimization is then applied to enhance key elements of the framework, including the neural network structure and input variables such as the extracted components of photovoltaic data and weather parameters, along with their historical values. This optimization process efficiently identifies the near-optimal model structure, resulting in an improved forecaster whose performance is validated using real-world data measured in El-Oued. The proposed framework demonstrates impressive accuracy, with a Normalized Root Mean Squared Error ranging from 2.96% to 4.8% for annual forecasts, 2.28% for summer forecasts, and 4.97% for generalization ability. Similarly, the Normalized Mean Absolute Error ranges from 1.89% to 2.89% for annual forecasts, 1.61% for summer forecasts, and 3.76% for generalization ability. The correlation coefficient is outstanding, between 99.9% and 99.96% for annual forecasts, reaching 99.97% for summer forecasts, and 99.67% for generalization ability. The study confirms the effectiveness of the proposed framework in enhancing network stability and power distribution.
{"title":"Improving short-term photovoltaic power forecasting with an evolving neural network incorporating time-varying filtering based on empirical mode decomposition","authors":"Mokhtar Ghodbane , Naima El-Amarty , Boussad Boumeddane , Fayaz Hussain , Hakim El Fadili , Saad Dosse Bennani , Mohamed Akil","doi":"10.1016/j.enconman.2024.119261","DOIUrl":"10.1016/j.enconman.2024.119261","url":null,"abstract":"<div><div>Accurately forecasting photovoltaic power generation is essential for the efficient integration of renewable energy into power grids. This paper presents a novel, high-accuracy framework for short-term photovoltaic productivity forecasting, tailored to the climatic conditions of the Algerian region of El-Oued. The framework automatically adapts the neural network forecast using a nature-inspired algorithm, eliminating the need for manual adjustments. It first addresses the complex, non-stationary nature of photovoltaic generation by incorporating a time-varying filter based on empirical mode decomposition, which decomposes the original photovoltaic data into multiple low-frequency components. Particle swarm optimization is then applied to enhance key elements of the framework, including the neural network structure and input variables such as the extracted components of photovoltaic data and weather parameters, along with their historical values. This optimization process efficiently identifies the near-optimal model structure, resulting in an improved forecaster whose performance is validated using real-world data measured in El-Oued. The proposed framework demonstrates impressive accuracy, with a Normalized Root Mean Squared Error ranging from 2.96% to 4.8% for annual forecasts, 2.28% for summer forecasts, and 4.97% for generalization ability. Similarly, the Normalized Mean Absolute Error ranges from 1.89% to 2.89% for annual forecasts, 1.61% for summer forecasts, and 3.76% for generalization ability. The correlation coefficient is outstanding, between 99.9% and 99.96% for annual forecasts, reaching 99.97% for summer forecasts, and 99.67% for generalization ability. The study confirms the effectiveness of the proposed framework in enhancing network stability and power distribution.</div></div>","PeriodicalId":11664,"journal":{"name":"Energy Conversion and Management","volume":"323 ","pages":"Article 119261"},"PeriodicalIF":9.9,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655850","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 : 2024-11-14DOI: 10.1016/j.enconman.2024.119259
Poh Ai Saw , Abdul Patah Muhamad Fazly , Wan Mohd Ashri Wan Daud , Zulhelmi Amir , Dania Qarrina Azman , Nurul Izzah Ahamed Kameel
The escalating accumulation of plastic waste presents a critical environmental challenge due to its resistance to degradation. Liquefaction, a thermochemical conversion process, emerges as a promising solution to convert plastic waste into valuable resources like fuel. The objective of this study was to investigate the behaviour of plastic polymer degradation in solvothermal liquefaction. This study comprehensively examines the liquefaction processes HDPE, LDPE, PS, and PP under 350–400 °C conditions and 30–90 min reaction times, using toluene as a solvent in an autoclave batch reactor. The results indicate that temperature significantly impacts liquefaction efficiency, with the following sequence: PS > PP > LDPE > HDPE. The liquefied products exhibit high heating values (HHV) of 40–44 MJ/kg, with viscosity and density comparable to gasoline and diesel. GC–MS and FTIR analyses reveal a composition rich in olefins, paraffins, and aromatics, producing carbon chain lengths from C6 to C20, aligning with conventional fuel. Finally, the mechanism of liquefaction for the polymers is proposed based on the chemical components found.
{"title":"Thermochemical liquefaction of thermoplastic into fuel using toluene: Product distribution and behaviour","authors":"Poh Ai Saw , Abdul Patah Muhamad Fazly , Wan Mohd Ashri Wan Daud , Zulhelmi Amir , Dania Qarrina Azman , Nurul Izzah Ahamed Kameel","doi":"10.1016/j.enconman.2024.119259","DOIUrl":"10.1016/j.enconman.2024.119259","url":null,"abstract":"<div><div>The escalating accumulation of plastic waste presents a critical environmental challenge due to its resistance to degradation. Liquefaction, a thermochemical conversion process, emerges as a promising solution to convert plastic waste into valuable resources like fuel. The objective of this study was to investigate the behaviour of plastic polymer degradation in solvothermal liquefaction. This study comprehensively examines the liquefaction processes HDPE, LDPE, PS, and PP under 350–400 °C conditions and 30–90 min reaction times, using toluene as a solvent in an autoclave batch reactor. The results indicate that temperature significantly impacts liquefaction efficiency, with the following sequence: PS > PP > LDPE > HDPE. The liquefied products exhibit high heating values (HHV) of 40–44 MJ/kg, with viscosity and density comparable to gasoline and diesel. GC–MS and FTIR analyses reveal a composition rich in olefins, paraffins, and aromatics, producing carbon chain lengths from C<sub>6</sub> to C<sub>20</sub>, aligning with conventional fuel. Finally, the mechanism of liquefaction for the polymers is proposed based on the chemical components found.</div></div>","PeriodicalId":11664,"journal":{"name":"Energy Conversion and Management","volume":"323 ","pages":"Article 119259"},"PeriodicalIF":9.9,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655900","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 : 2024-11-14DOI: 10.1016/j.enconman.2024.119220
Xinyuan Shao , Jonas W. Ringsberg , Erland Johnson , Zhiyuan Li , Hua-Dong Yao , Jan G. Skjoldhammer , Stefan Björklund
A wave energy converter (WEC) comprises many components with distinct functions. The whole WEC system is complicated, as each component is also a complex subsystem. It is challenging to properly model and couple these subsystems to achieve a global simulation of the whole system. This study proposes an FMI-based co-simulation framework to tackle this challenge. Through the use of a co-simulation technique requiring minimal programming effort, a suite of numerical solvers serving for modelling various WEC components is coupled to create a comprehensive system model for a single WEC unit. The modules of the Ansys software, Aqwa and Rigid Dynamics, are employed to model hydrodynamic loads and motion responses. Simulink is utilized to model the power take-off (PTO) system and then integrate all models into a global simulation. The capability and accuracy of the FMI-based co-simulation framework are validated against an experimental heave decay test and verified by cross-comparing a numerical model built in SESAM. Furthermore, the framework is expanded to encompass the modelling of a large-scale wave park that includes multiple WEC units. Based on a novel WEC concept called NoviOcean, two study cases of a single unit and an 18-unit wave park are investigated. Buoy motions and power performance under several regular and irregular sea states are analysed. The hydrodynamic interactions between the units are evaluated quantitatively regarding the power performance. It is found that the interactions improve the power performance, with a maximum increase of up to 36%.
{"title":"An FMI-based co-simulation framework for simulations of wave energy converter systems","authors":"Xinyuan Shao , Jonas W. Ringsberg , Erland Johnson , Zhiyuan Li , Hua-Dong Yao , Jan G. Skjoldhammer , Stefan Björklund","doi":"10.1016/j.enconman.2024.119220","DOIUrl":"10.1016/j.enconman.2024.119220","url":null,"abstract":"<div><div>A wave energy converter (WEC) comprises many components with distinct functions. The whole WEC system is complicated, as each component is also a complex subsystem. It is challenging to properly model and couple these subsystems to achieve a global simulation of the whole system. This study proposes an FMI-based co-simulation framework to tackle this challenge. Through the use of a co-simulation technique requiring minimal programming effort, a suite of numerical solvers serving for modelling various WEC components is coupled to create a comprehensive system model for a single WEC unit. The modules of the Ansys software, Aqwa and Rigid Dynamics, are employed to model hydrodynamic loads and motion responses. Simulink is utilized to model the power take-off (PTO) system and then integrate all models into a global simulation. The capability and accuracy of the FMI-based co-simulation framework are validated against an experimental heave decay test and verified by cross-comparing a numerical model built in SESAM. Furthermore, the framework is expanded to encompass the modelling of a large-scale wave park that includes multiple WEC units. Based on a novel WEC concept called NoviOcean, two study cases of a single unit and an 18-unit wave park are investigated. Buoy motions and power performance under several regular and irregular sea states are analysed. The hydrodynamic interactions between the units are evaluated quantitatively regarding the power performance. It is found that the interactions improve the power performance, with a maximum increase of up to 36%.</div></div>","PeriodicalId":11664,"journal":{"name":"Energy Conversion and Management","volume":"323 ","pages":"Article 119220"},"PeriodicalIF":9.9,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655849","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 : 2024-11-14DOI: 10.1016/j.enconman.2024.119268
Nguyen Van Toan , Yijie Li , Truong Thi Kim Tuoi , Nuur Syahidah Sabran , Jun Hieng Kiat , Ioana Voiculescu , Takahito Ono
Thermoelectric generators (TEGs) offer a promising solution for converting waste heat into electrical energy, addressing global energy challenges with their ability to operate without moving parts and under diverse environmental conditions. However, the adoption of TEGs is limited by the drawbacks of traditional materials like bismuth telluride, which are expensive and environmentally hazardous. Silicon-based TEGs, while abundant and compatible with semiconductor manufacturing, are characterized by low thermoelectric efficiency due to high thermal conductivity and complex fabrication. In this study, we explore the possibility to use nanoporous silicon, fabricated through a metal-assisted chemical etching (MACE) method, as a novel material for TEGs. Our hypothesis was that nanoporous structures would reduce thermal conductivity and enhance the Seebeck coefficient, thereby improving the figure of merit (ZT). Additionally, a spin-on dopant (SOD) technique was used to improve the contact resistance, and further enhance the device’s performance. This research presents the synthesis and detailed characterization of nanoporous silicon, with a focus on optimizing porosity and layer thickness. The effects of SOD treatment on the electrical properties are also evaluated. The fabricated nanoporous silicon-based micro-TEGs exhibited ZT values that were 4.2 times higher for n-type and 12.4 times larger for p-type compared to bulk silicon, achieving a maximum power density of 1.12 μW/cm2. This performance significantly surpassed that of bulk silicon devices. These findings demonstrated the potential of nanoporous silicon as a viable material for next-generation thermoelectric applications, offering a scalable and more environmentally friendly alternative to traditional thermoelectric materials.
{"title":"Thermoelectric generator using nanoporous silicon formed by metal-assisted chemical etching method","authors":"Nguyen Van Toan , Yijie Li , Truong Thi Kim Tuoi , Nuur Syahidah Sabran , Jun Hieng Kiat , Ioana Voiculescu , Takahito Ono","doi":"10.1016/j.enconman.2024.119268","DOIUrl":"10.1016/j.enconman.2024.119268","url":null,"abstract":"<div><div>Thermoelectric generators (TEGs) offer a promising solution for converting waste heat into electrical energy, addressing global energy challenges with their ability to operate without moving parts and under diverse environmental conditions. However, the adoption of TEGs is limited by the drawbacks of traditional materials like bismuth telluride, which are expensive and environmentally hazardous. Silicon-based TEGs, while abundant and compatible with semiconductor manufacturing, are characterized by low thermoelectric efficiency due to high thermal conductivity and complex fabrication. In this study, we explore the possibility to use nanoporous silicon, fabricated through a metal-assisted chemical etching (MACE) method, as a novel material for TEGs. Our hypothesis was that nanoporous structures would reduce thermal conductivity and enhance the Seebeck coefficient, thereby improving the figure of merit (ZT). Additionally, a spin-on dopant (SOD) technique was used to improve the contact resistance, and further enhance the device’s performance. This research presents the synthesis and detailed characterization of nanoporous silicon, with a focus on optimizing porosity and layer thickness. The effects of SOD treatment on the electrical properties are also evaluated. The fabricated nanoporous silicon-based micro-TEGs exhibited ZT values that were 4.2 times higher for n-type and 12.4 times larger for p-type compared to bulk silicon, achieving a maximum power density of 1.12 μW/cm<sup>2</sup>. This performance significantly surpassed that of bulk silicon devices. These findings demonstrated the potential of nanoporous silicon as a viable material for next-generation thermoelectric applications, offering a scalable and more environmentally friendly alternative to traditional thermoelectric materials.</div></div>","PeriodicalId":11664,"journal":{"name":"Energy Conversion and Management","volume":"323 ","pages":"Article 119268"},"PeriodicalIF":9.9,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655897","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 : 2024-11-13DOI: 10.1016/j.enconman.2024.119218
Kai Wang , Shuo Shan , Weijing Dou , Haikun Wei , Kanjian Zhang
Accurate photovoltaic (PV) power forecasting improves grid stability and energy utilization efficiency. Integrating large-scale cloud information from satellite imagery can enhance the accuracy of ultra-short-term PV power forecasts. However, existing satellite-based forecasting methods consider the global features of satellite images but overlook the impact of localized cloud movements on future PV generation in the target area. The focus on local information, such as PV time series and nearby clouds in the region of interest, contributes to more efficient feature extraction of satellite images. In this study, a deep learning method is proposed to strengthen the cross-modal correlation of global and local information in satellite image encoding and the multi-modal fusion stage. A novel satellite image encoder is designed by using the dual-branch spatio-temporal vision transformer to compress large-scale cloud features into the features of the region of interest. Satellite image features are then combined with PV time-series features using a cross transformer with rotary position embedding. The proposed method was validated using data from ten PV stations, demonstrating forecast skill of 47.29%–58.23% for PV power forecasts up to 4 h ahead. Compared to ViT, ViViT, CrossViT, and Perceiver, the proposed method achieves an average improvement of 2.39%–3.75%, and a minimum of 8.98% improvement in scenarios where PV time-series data is unavailable. Moreover, the proposed method outperforms the state-of-the-art methods by 2.85%–5.53%. The experimental results highlight that the proposed method shows accurate and robust forecasting performance and is a reliable alternative to PV power forecasting.
{"title":"A cross-modal deep learning method for enhancing photovoltaic power forecasting with satellite imagery and time series data","authors":"Kai Wang , Shuo Shan , Weijing Dou , Haikun Wei , Kanjian Zhang","doi":"10.1016/j.enconman.2024.119218","DOIUrl":"10.1016/j.enconman.2024.119218","url":null,"abstract":"<div><div>Accurate photovoltaic (PV) power forecasting improves grid stability and energy utilization efficiency. Integrating large-scale cloud information from satellite imagery can enhance the accuracy of ultra-short-term PV power forecasts. However, existing satellite-based forecasting methods consider the global features of satellite images but overlook the impact of localized cloud movements on future PV generation in the target area. The focus on local information, such as PV time series and nearby clouds in the region of interest, contributes to more efficient feature extraction of satellite images. In this study, a deep learning method is proposed to strengthen the cross-modal correlation of global and local information in satellite image encoding and the multi-modal fusion stage. A novel satellite image encoder is designed by using the dual-branch spatio-temporal vision transformer to compress large-scale cloud features into the features of the region of interest. Satellite image features are then combined with PV time-series features using a cross transformer with rotary position embedding. The proposed method was validated using data from ten PV stations, demonstrating forecast skill of 47.29%–58.23% for PV power forecasts up to 4 h ahead. Compared to ViT, ViViT, CrossViT, and Perceiver, the proposed method achieves an average improvement of 2.39%–3.75%, and a minimum of 8.98% improvement in scenarios where PV time-series data is unavailable. Moreover, the proposed method outperforms the state-of-the-art methods by 2.85%–5.53%. The experimental results highlight that the proposed method shows accurate and robust forecasting performance and is a reliable alternative to PV power forecasting.</div></div>","PeriodicalId":11664,"journal":{"name":"Energy Conversion and Management","volume":"323 ","pages":"Article 119218"},"PeriodicalIF":9.9,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655970","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 : 2024-11-13DOI: 10.1016/j.enconman.2024.119240
Santeri Siren , Janne Hirvonen , Piia Sormunen
While ground source heat pump systems offer an energy-efficient means of generating local renewable energy for buildings, they also face challenges, such as ground thermal imbalance and the spatial requirements of the bore field. These problems can be addressed by optimizing the bore field configuration and coupling the system with complementary energy sources. This study explores the relationship between the bore field configuration and the long-term performance of an ambient air-assisted hybrid ground source heat pump system. The hypothesis was that utilizing ambient air as a supplementary heat source effectively reduces the significance of the bore field configuration on the techno-economic performance of the system. Understanding this relationship can aid in designing more efficient systems. This paper presents quantitative effects of bore field layout and borehole spacing on the performance of AAA-GSHP systems, using several different performance metrics. The analysis encompassed various bore field configurations assessed for a traditional and an ambient air-assisted ground source heat pump system using dynamic energy simulations for a 50-year period with IDA ICE software. A key finding was that utilizing ambient air as an additional heat source highly effectively mitigates the effects of the bore field layout and spacing on the techno-economic performance of the system. By decreasing borehole spacing from 15 m to 5 m, the required land area was reduced by 89 % while simultaneously achieving a 25 % higher share of renewable energy production compared to the traditional solution. Depending on the bore field configuration, the ambient air-assisted system achieved a 0–31 % lower levelized cost of energy, 2–52 % lower CO2 emissions, and a 9–58 % higher share of renewable energy production compared to the traditional system. The achieved benefits were particularly substantial with configurations where numerous boreholes were concentrated in a small land area. On average, 40 % of the thermal energy from the ambient air was charged in the bore field, while the remaining portion was utilized directly in the evaporator. The conversion of a traditional system to an ambient air-assisted system can be achieved with a technically straightforward solution that leverages existing technology, increasing the initial investment by only 6 %. The ambient air-assisted ground source heat pump system shows significant potential for applications with a year-round heating demand and limited land area for bore hole installation.
{"title":"Comparison of traditional and ambient air-assisted ground source heat pump systems using different bore field configurations","authors":"Santeri Siren , Janne Hirvonen , Piia Sormunen","doi":"10.1016/j.enconman.2024.119240","DOIUrl":"10.1016/j.enconman.2024.119240","url":null,"abstract":"<div><div>While ground source heat pump systems offer an energy-efficient means of generating local renewable energy for buildings, they also face challenges, such as ground thermal imbalance and the spatial requirements of the bore field. These problems can be addressed by optimizing the bore field configuration and coupling the system with complementary energy sources. This study explores the relationship between the bore field configuration and the long-term performance of an ambient air-assisted hybrid ground source heat pump system. The hypothesis was that utilizing ambient air as a supplementary heat source effectively reduces the significance of the bore field configuration on the techno-economic performance of the system. Understanding this relationship can aid in designing more efficient systems. This paper presents quantitative effects of bore field layout and borehole spacing on the performance of AAA-GSHP systems, using several different performance metrics. The analysis encompassed various bore field configurations assessed for a traditional and an ambient air-assisted ground source heat pump system using dynamic energy simulations for a 50-year period with IDA ICE software. A key finding was that utilizing ambient air as an additional heat source highly effectively mitigates the effects of the bore field layout and spacing on the techno-economic performance of the system. By decreasing borehole spacing from 15 m to 5 m, the required land area was reduced by 89 % while simultaneously achieving a 25 % higher share of renewable energy production compared to the traditional solution. Depending on the bore field configuration, the ambient air-assisted system achieved a 0–31 % lower levelized cost of energy, 2–52 % lower CO<sub>2</sub> emissions, and a 9–58 % higher share of renewable energy production compared to the traditional system. The achieved benefits were particularly substantial with configurations where numerous boreholes were concentrated in a small land area. On average, 40 % of the thermal energy from the ambient air was charged in the bore field, while the remaining portion was utilized directly in the evaporator. The conversion of a traditional system to an ambient air-assisted system can be achieved with a technically straightforward solution that leverages existing technology, increasing the initial investment by only 6 %. The ambient air-assisted ground source heat pump system shows significant potential for applications with a year-round heating demand and limited land area for bore hole installation.</div></div>","PeriodicalId":11664,"journal":{"name":"Energy Conversion and Management","volume":"323 ","pages":"Article 119240"},"PeriodicalIF":9.9,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655964","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}
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