Pub Date : 2024-10-01DOI: 10.1016/j.ecmx.2024.100748
Zhi-Xuan Dai , Chun-Yu Chen
This study explores the development of an energy harvesting chip (EHC) using a complementary metal oxide semiconductor (CMOS) process, addressing the need for efficient micro-scale energy harvesters in modern electronics. The EHC integrates a thermoelectric energy harvester (TEH) and a photovoltaic energy harvester (PEH) to maximize energy conversion efficiency. A key challenge in TEH design is enhancing power output, which is addressed by suspending the cold ends of 41 thermocouples within the TEH structure through post-processing. Experimental methods were employed to assess the performance of the TEH, revealing an output voltage of 21.4 mV and a maximum output power of 9.32 nW under a 3 K temperature difference. The TEH demonstrated a voltage factor of 8.9 mV/(mm2·K) and a power factor of 1.3 nW/(mm2·K2). The PEH was designed with novel patterned p-n junctions, integrating lightly doped n-type regions with interdigitated p-type doping to increase junction density, resulting in high conversion efficiency. The experimental results confirm the effectiveness of the EHC design, showcasing its potential in energy harvesting applications.
本研究探讨了利用互补金属氧化物半导体（CMOS）工艺开发能量收集芯片（EHC）的问题，以满足现代电子产品对高效微型能量收集器的需求。EHC 集成了热电能量收集器 (TEH) 和光电能量收集器 (PEH)，以最大限度地提高能量转换效率。热电能量收集器设计中的一个关键挑战是提高功率输出，通过后处理将 41 个热电偶的冷端悬挂在热电能量收集器结构中可以解决这个问题。实验方法用于评估 TEH 的性能，结果显示，在 3 K 温差下，输出电压为 21.4 mV，最大输出功率为 9.32 nW。TEH 的电压系数为 8.9 mV/（mm2-K），功率系数为 1.3 nW/（mm2-K2）。PEH 采用新颖的图案化 p-n 结设计，将轻度掺杂的 n 型区与相互掺杂的 p 型区整合在一起，以提高结密度，从而实现高转换效率。实验结果证实了 EHC 设计的有效性，展示了其在能量收集应用中的潜力。
{"title":"Fabrication and evaluation of a CMOS-based energy harvesting chip integrating photovoltaic and thermoelectric energy harvesters","authors":"Zhi-Xuan Dai , Chun-Yu Chen","doi":"10.1016/j.ecmx.2024.100748","DOIUrl":"10.1016/j.ecmx.2024.100748","url":null,"abstract":"<div><div>This study explores the development of an energy harvesting chip (EHC) using a complementary metal oxide semiconductor (CMOS) process, addressing the need for efficient micro-scale energy harvesters in modern electronics. The EHC integrates a thermoelectric energy harvester (TEH) and a photovoltaic energy harvester (PEH) to maximize energy conversion efficiency. A key challenge in TEH design is enhancing power output, which is addressed by suspending the cold ends of 41 thermocouples within the TEH structure through post-processing. Experimental methods were employed to assess the performance of the TEH, revealing an output voltage of 21.4 mV and a maximum output power of 9.32 nW under a 3 K temperature difference. The TEH demonstrated a voltage factor of 8.9 mV/(mm<sup>2</sup>·K) and a power factor of 1.3 nW/(mm<sup>2</sup>·K<sup>2</sup>). The PEH was designed with novel patterned p-n junctions, integrating lightly doped n-type regions with interdigitated p-type doping to increase junction density, resulting in high conversion efficiency. The experimental results confirm the effectiveness of the EHC design, showcasing its potential in energy harvesting applications.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"24 ","pages":"Article 100748"},"PeriodicalIF":7.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420630","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 adoption of carbon capture systems presents a pivotal strategy for mitigating greenhouse gas emissions, notably carbon dioxide. Nevertheless, the substantial surge in energy consumption associated with such systems remains a significant challenge. Addressing this challenge necessitates the integration of renewable energy sources. This study is dedicated to optimizing the conventional post-combustion carbon capture configuration, focusing on energy, exergy, and exergoeconomic considerations. The optimized configuration showcases a noteworthy 10 % reduction in overall energy penalties compared to its conventional counterpart, primarily attributed to diminished energy utilization in the reboiler. To achieve absolute sustainability and eliminate energy penalties in the optimized configuration, integration of a parabolic trough collector for steam provision to the reboiler and photovoltaic solar collectors for powering the plant’s equipment was undertaken. Furthermore, the incorporation of solar thermal storage tanks and batteries enables the storage of excess heat and electricity, ensuring operational continuity for up to 13 h in the absence of sunlight, such as during nighttime. The final optimized configuration manifests a commendable 14 % enhancement in exergoeconomic performance relative to the conventional configuration, thereby realizing zero energy penalties. This achievement renders the optimized configuration a compelling and viable choice for carbon capture units.
{"title":"Achieving net zero energy penalty in post-combustion carbon capture through solar Energy: Parabolic trough and photovoltaic technologies","authors":"Farzin Hosseinifard , Milad Hosseinpour , Mohsen Salimi , Majid Amidpour","doi":"10.1016/j.ecmx.2024.100757","DOIUrl":"10.1016/j.ecmx.2024.100757","url":null,"abstract":"<div><div>The adoption of carbon capture systems presents a pivotal strategy for mitigating greenhouse gas emissions, notably carbon dioxide. Nevertheless, the substantial surge in energy consumption associated with such systems remains a significant challenge. Addressing this challenge necessitates the integration of renewable energy sources. This study is dedicated to optimizing the conventional post-combustion carbon capture configuration, focusing on energy, exergy, and exergoeconomic considerations. The optimized configuration showcases a noteworthy 10 % reduction in overall energy penalties compared to its conventional counterpart, primarily attributed to diminished energy utilization in the reboiler. To achieve absolute sustainability and eliminate energy penalties in the optimized configuration, integration of a parabolic trough collector for steam provision to the reboiler and photovoltaic solar collectors for powering the plant’s equipment was undertaken. Furthermore, the incorporation of solar thermal storage tanks and batteries enables the storage of excess heat and electricity, ensuring operational continuity for up to 13 h in the absence of sunlight, such as during nighttime. The final optimized configuration manifests a commendable 14 % enhancement in exergoeconomic performance relative to the conventional configuration, thereby realizing zero energy penalties. This achievement renders the optimized configuration a compelling and viable choice for carbon capture units.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"24 ","pages":"Article 100757"},"PeriodicalIF":7.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142539791","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.2024.100724
Malik Ali Judge , Vincenzo Franzitta , Domenico Curto , Andrea Guercio , Giansalvo Cirrincione , Hasan Ali Khattak
Technological advancements, urbanization, high energy demand, and global requirements to mitigate carbon footprints have led to the adoption of innovative green technologies for energy production. The integration of green technologies with traditional grids offers huge benefits. This amalgamation may bring a power mismatch dilemma due to intermittent renewable energy production and nonlinear energy consumption patterns which can affect the whole system’s reliability and operational efficiency. An efficient Energy Management System (EMS) is essential to deal with uncertainties associated with renewable energy production and load demand while optimizing the operation of distributed energy generation sources. This state-of-the-art review presents artificial intelligence-based solutions to improve EMS, focusing on optimal scheduling of generation sources, forecasting load and renewable energy production, and multi-agent-based decentralized control. The review’s finding suggests that the advanced metaheuristic algorithms can overcome challenges of trapping in local optima and premature convergence and due to this, they are now widely adopted and effectively utilized in scheduling problems. To mitigate uncertainties of renewable energy production and load demand, the long short-term memory and convolutional neural networks can manage spatiotemporal characteristics of renewable and load datasets and forecast highly accurate results. The multi-agent-based system offers a distributed control to complex problems that are computationally less expensive and outperforms centralized approaches. The increased use of advanced metaheuristic optimization techniques and hybrid machine learning and deep learning models is observed for optimization and forecasting applications. The advanced metaheuristic algorithms are a good addition to the literature, they are still in emerging stages and their performance can further be improved. This review also presents the decentralized and centralized EMS-based energy-sharing mechanism between interconnected micro grids. The use of advanced forecasting and metaheuristic algorithms can potentially handle the stochastic nature of renewable energy production and load demand.
{"title":"A comprehensive review of artificial intelligence approaches for smart grid integration and optimization","authors":"Malik Ali Judge , Vincenzo Franzitta , Domenico Curto , Andrea Guercio , Giansalvo Cirrincione , Hasan Ali Khattak","doi":"10.1016/j.ecmx.2024.100724","DOIUrl":"10.1016/j.ecmx.2024.100724","url":null,"abstract":"<div><div>Technological advancements, urbanization, high energy demand, and global requirements to mitigate carbon footprints have led to the adoption of innovative green technologies for energy production. The integration of green technologies with traditional grids offers huge benefits. This amalgamation may bring a power mismatch dilemma due to intermittent renewable energy production and nonlinear energy consumption patterns which can affect the whole system’s reliability and operational efficiency. An efficient Energy Management System (EMS) is essential to deal with uncertainties associated with renewable energy production and load demand while optimizing the operation of distributed energy generation sources. This state-of-the-art review presents artificial intelligence-based solutions to improve EMS, focusing on optimal scheduling of generation sources, forecasting load and renewable energy production, and multi-agent-based decentralized control. The review’s finding suggests that the advanced metaheuristic algorithms can overcome challenges of trapping in local optima and premature convergence and due to this, they are now widely adopted and effectively utilized in scheduling problems. To mitigate uncertainties of renewable energy production and load demand, the long short-term memory and convolutional neural networks can manage spatiotemporal characteristics of renewable and load datasets and forecast highly accurate results. The multi-agent-based system offers a distributed control to complex problems that are computationally less expensive and outperforms centralized approaches. The increased use of advanced metaheuristic optimization techniques and hybrid machine learning and deep learning models is observed for optimization and forecasting applications. The advanced metaheuristic algorithms are a good addition to the literature, they are still in emerging stages and their performance can further be improved. This review also presents the decentralized and centralized EMS-based energy-sharing mechanism between interconnected micro grids. The use of advanced forecasting and metaheuristic algorithms can potentially handle the stochastic nature of renewable energy production and load demand.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"24 ","pages":"Article 100724"},"PeriodicalIF":7.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142703973","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.2024.100746
Ryan Spragg , Xianglin Li
This work optimizes the performance of the direct methanol fuel cell (DMFC) to increase its efficiency and strengthen its validity in portable power generation. Specifically, this work focuses on optimizing vapor-feed supply techniques and incorporating water management layers (WMLs) to analyze their effect on methanol crossover. The significance of the vapor-feed supply technique is to enhance the reaction kinetics of the methanol oxidation reaction (MOR) and enable the use of pure methanol (MeOH) as a fuel. Pure methanol is the ideal fuel for the DMFC as it has the highest possible energy density compared to dilute concentrations. However, use of pure methanol is hindered by methanol crossover, which is regarded as the largest technical barrier to commercializing DMFCs. This study measured methanol crossover through a CO2 sensor attached to the cathode outlet and added hydrophobic WMLs to the cathode to alleviate the methanol crossover. The hydrophobic WMLs increased the mass transfer resistance to generate a pressure gradient that encourages water backflow for use in both the proton exchange membrane (PEM) and anode reactions. The influence of vapor flow rate and fuel concentration will also be explored to show their impact on performance and methanol crossover. Likewise, long-term consumption and durability tests were conducted with and without a WML to dictate the WML’s superior fuel efficiency, total efficiency, energy density, and reduced methanol crossover using pure methanol. The addition of the WML increased the energy density of the vapor feed DMFC, using pure methanol, from 705.9 Wh kgMeOH-1 to 867.7 Wh kgMeOH-1 and lowered the crossover current density by 14.8 % when discharged at a constant 200 mA cm−2.
{"title":"Vapor-feed direct methanol fuel cells using pure methanol","authors":"Ryan Spragg , Xianglin Li","doi":"10.1016/j.ecmx.2024.100746","DOIUrl":"10.1016/j.ecmx.2024.100746","url":null,"abstract":"<div><div>This work optimizes the performance of the direct methanol fuel cell (DMFC) to increase its efficiency and strengthen its validity in portable power generation. Specifically, this work focuses on optimizing vapor-feed supply techniques and incorporating water management layers (WMLs) to analyze their effect on methanol crossover. The significance of the vapor-feed supply technique is to enhance the reaction kinetics of the methanol oxidation reaction (MOR) and enable the use of pure methanol (MeOH) as a fuel. Pure methanol is the ideal fuel for the DMFC as it has the highest possible energy density compared to dilute concentrations. However, use of pure methanol is hindered by methanol crossover, which is regarded as the largest technical barrier to commercializing DMFCs. This study measured methanol crossover through a CO<sub>2</sub> sensor attached to the cathode outlet and added hydrophobic WMLs to the cathode to alleviate the methanol crossover. The hydrophobic WMLs increased the mass transfer resistance to generate a pressure gradient that encourages water backflow for use in both the proton exchange membrane (PEM) and anode reactions. The influence of vapor flow rate and fuel concentration will also be explored to show their impact on performance and methanol crossover. Likewise, long-term consumption and durability tests were conducted with and without a WML to dictate the WML’s superior fuel efficiency, total efficiency, energy density, and reduced methanol crossover using pure methanol. The addition of the WML increased the energy density of the vapor feed DMFC, using pure methanol, from 705.9 Wh kg<sub>MeOH</sub><sup>-1</sup> to 867.7 Wh kg<sub>MeOH</sub><sup>-1</sup> and lowered the crossover current density by 14.8 % when discharged at a constant 200 mA cm<sup>−2</sup>.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"24 ","pages":"Article 100746"},"PeriodicalIF":7.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142704503","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.2024.100815
Magda I. El-Afifi , Bishoy E. Sedhom , Abdelfattah A. Eladl , Padmanaban Sanjeevikumar , Samaa Fawzy
This study investigates a novel approach to improve energy efficiency through a Demand Response (DR) program with a Game Theory (GT)-based Time-of-Use (ToU) pricing model. While traditional DR programs encourage consumption shifts towards off-peak periods, they utilize a flat pricing structure. This means all users pay the same regardless of their individual load contribution during peak times, where prices fluctuate based on demand exceeding generation capacity. The proposed GT-based ToU model addresses this by establishing dynamic on-peak and shoulder-peak hour rates tailored to each user’s consumption profile. This personalized pricing incentivizes a more targeted shift away from peak hours, potentially leading to further efficiency gains. The model’s effectiveness is evaluated against the existing ToU system and the current day-ahead real-time pricing scheme. Additionally, the study acknowledges the potential for increased demand during off-peak hours due to load shifting. To address this, the influence of two optimization algorithms, Genetic Algorithm (GA) and Archimedes Optimization Algorithm (AOA), on user electricity bills and peak-to-average ratio following load scheduling is examined. The research concludes by demonstrating the superiority of the GT-based ToU model and highlighting AOA’s superior performance compared to GA in optimizing these factors.
{"title":"Leveraging Game Theory to Design Incentive-Compatible Time-Varying electricity pricing with Demand-Side management","authors":"Magda I. El-Afifi , Bishoy E. Sedhom , Abdelfattah A. Eladl , Padmanaban Sanjeevikumar , Samaa Fawzy","doi":"10.1016/j.ecmx.2024.100815","DOIUrl":"10.1016/j.ecmx.2024.100815","url":null,"abstract":"<div><div>This study investigates a novel approach to improve energy efficiency through a Demand Response (DR) program with a Game Theory (GT)-based Time-of-Use (ToU) pricing model. While traditional DR programs encourage consumption shifts towards off-peak periods, they utilize a flat pricing structure. This means all users pay the same regardless of their individual load contribution during peak times, where prices fluctuate based on demand exceeding generation capacity. The proposed GT-based ToU model addresses this by establishing dynamic on-peak and shoulder-peak hour rates tailored to each user’s consumption profile. This personalized pricing incentivizes a more targeted shift away from peak hours, potentially leading to further efficiency gains. The model’s effectiveness is evaluated against the existing ToU system and the current day-ahead real-time pricing scheme. Additionally, the study acknowledges the potential for increased demand during off-peak hours due to load shifting. To address this, the influence of two optimization algorithms, Genetic Algorithm (GA) and Archimedes Optimization Algorithm (AOA), on user electricity bills and peak-to-average ratio following load scheduling is examined. The research concludes by demonstrating the superiority of the GT-based ToU model and highlighting AOA’s superior performance compared to GA in optimizing these factors.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"24 ","pages":"Article 100815"},"PeriodicalIF":7.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142747639","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.2024.100811
Bryan Higgins, Lincoln Pratson, Dalia Patiño-Echeverri
Enhanced Geothermal Systems (EGS) produce renewable power that can complement variable renewable sources such as wind and solar, but EGS is expensive to deploy due to high cost of drilling geothermal wells. Repurposing abandoned or declining oil and gas wells for use in EGS is one option that can lower project capital costs by making use of existing wells and infrastructure. We assess the feasibility of this option using an oil and gas well database for the state of New Mexico. We begin by defining physical and geographic criteria for selecting existing wells suitable for EGS. We then identify clusters of nearby wells that could support separate EGS projects. Finally, we use the National Renewable Energy Laboratory’s System Advisor Model to estimate the performance and costs of these potential projects. Through our analysis, we find that 543 existing oil and gas wells in the state, comprising 0.4 % of the total number of oil and gas wells drilled in New Mexico, could be used to form 113 EGS projects with a cumulative generating capacity of 361 MW. The modeled Levelized Cost of Electricity of the repurposed EGS projects was 31–70 % lower than that of identical projects using newly drilled wells at the same locations, with the most cost-effective projects having the highest number of wells that can be clustered for higher power output. A major reason for the relatively low number of suitable wells is that oil and gas reservoirs are typically located in cooler geothermal systems, constraining harvestable energy and conversion efficiencies. Nonetheless, potential EGS projects drawing upon fewer existing wells still produce significant heat energy even if it is not enough to generate electricity, so exploring other ways this energy might be used economically is important. The method we present for assessing first-order feasibility and costs of well repurposing for EGS can be applied to any oil- and gas-producing region to identify well clusters where additional site-level investigations are warranted.
{"title":"Techno-economic assessment of repurposing oil & gas wells for Enhanced Geothermal Systems: A New Mexico, USA feasibility study","authors":"Bryan Higgins, Lincoln Pratson, Dalia Patiño-Echeverri","doi":"10.1016/j.ecmx.2024.100811","DOIUrl":"10.1016/j.ecmx.2024.100811","url":null,"abstract":"<div><div>Enhanced Geothermal Systems (EGS) produce renewable power that can complement variable renewable sources such as wind and solar, but EGS is expensive to deploy due to high cost of drilling geothermal wells. Repurposing abandoned or declining oil and gas wells for use in EGS is one option that can lower project capital costs by making use of existing wells and infrastructure. We assess the feasibility of this option using an oil and gas well database for the state of New Mexico. We begin by defining physical and geographic criteria for selecting existing wells suitable for EGS. We then identify clusters of nearby wells that could support separate EGS projects. Finally, we use the National Renewable Energy Laboratory’s System Advisor Model to estimate the performance and costs of these potential projects. Through our analysis, we find that 543 existing oil and gas wells in the state, comprising 0.4 % of the total number of oil and gas wells drilled in New Mexico, could be used to form 113 EGS projects with a cumulative generating capacity of 361 MW. The modeled Levelized Cost of Electricity of the repurposed EGS projects was 31–70 % lower than that of identical projects using newly drilled wells at the same locations, with the most cost-effective projects having the highest number of wells that can be clustered for higher power output. A major reason for the relatively low number of suitable wells is that oil and gas reservoirs are typically located in cooler geothermal systems, constraining harvestable energy and conversion efficiencies. Nonetheless, potential EGS projects drawing upon fewer existing wells still produce significant heat energy even if it is not enough to generate electricity, so exploring other ways this energy might be used economically is important. The method we present for assessing first-order feasibility and costs of well repurposing for EGS can be applied to any oil- and gas-producing region to identify well clusters where additional site-level investigations are warranted.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"24 ","pages":"Article 100811"},"PeriodicalIF":7.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142747641","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.2024.100756
Muntasir Shahabuddin, Nikolaos Kazantzis, Andrew R Teixeira, Michael T. Timko
Hydrothermal liquefaction (HTL) has remarkable potential for efficient conversion of abundant, decentralized organic wastes into renewable fuels. Because waste is a highly distributed resource with context-dependent economic viability, selection of optimal deployment sites is slowed by the need to develop detailed techno-economic analyses (TEA) for the thousands of potential deployment locations, each with their own unique combinates of scale, proximity to infrastructure/markets, and feedstock properties. An economic modeling framework that requires only easily obtainable inputs for assessing economic performance would therefore allow multiplexed analysis of many thousands of cases, whereas traditional TEA would not be possible for more than a handful of cases. Within such a context, the present study uses machine learning to guide development of a TEA and modeling framework which provides accurate cost predictions using three key inputs – feedstock cost, biocrude yield, and process scale – to estimate the minimum fuel selling price (MFSP) that an HTL process can achieve. The structure of the proposed framework is informed and based on empirical observations of cost projections made by a detailed TEA over a wide range of feedstock costs, biocrude yields, and process scales. A machine learning guided process was used to identify, train, and test a series of models using auto-generated data for training and independently reported data for testing. The most accurate model consists of three terms and requires 6 adjustable parameters to predict independently published values of MFSP (N = 28) to within an average value of ± 20.4%. It is demonstrated that the reduced-order model’s predictions fall within 40% of the corresponding published values 95% of the time, and in the worst case, the associated discrepancy is 45.9%, suggesting that the accuracy of the machine learned model is indeed comparable to the TEAs that were used to build it. Moreover, the terms in the model are physically interpretable, conferring greater reliability to the use of its predictions. The model can be used to predict the dependence of MSFP on biocrude yield, scale, and feedstock cost; interestingly, MFSP is insensitive to biocrude yield and/or scale under many situations of interest and identifying the critical value for a given application is crucial to optimizing economic performance. The proposed model can be also extended to evaluate economic performance of newly developed HTL-based processes, including catalytic HTL, and the methodological framework used in this study is deemed appropriate for the development of machine learned TEA models in cases of other similar waste-to-energy technologies.
{"title":"One techno-economic analysis to rule them all: Instant prediction of hydrothermal liquefaction economic performance with a machine learned analytic equation","authors":"Muntasir Shahabuddin, Nikolaos Kazantzis, Andrew R Teixeira, Michael T. Timko","doi":"10.1016/j.ecmx.2024.100756","DOIUrl":"10.1016/j.ecmx.2024.100756","url":null,"abstract":"<div><div>Hydrothermal liquefaction (HTL) has remarkable potential for efficient conversion of abundant, decentralized organic wastes into renewable fuels. Because waste is a highly distributed resource with context-dependent economic viability, selection of optimal deployment sites is slowed by the need to develop detailed techno-economic analyses (TEA) for the thousands of potential deployment locations, each with their own unique combinates of scale, proximity to infrastructure/markets, and feedstock properties. An economic modeling framework that requires only easily obtainable inputs for assessing economic performance would therefore allow multiplexed analysis of many thousands of cases, whereas traditional TEA would not be possible for more than a handful of cases. Within such a context, the present study uses machine learning to guide development of a TEA and modeling framework which provides accurate cost predictions using three key inputs – feedstock cost, biocrude yield, and process scale – to estimate the minimum fuel selling price (MFSP) that an HTL process can achieve. The structure of the proposed framework is informed and based on empirical observations of cost projections made by a detailed TEA over a wide range of feedstock costs, biocrude yields, and process scales. A machine learning guided process was used to identify, train, and test a series of models using auto-generated data for training and independently reported data for testing. The most accurate model consists of three terms and requires 6 adjustable parameters to predict independently published values of MFSP (<em>N</em> = 28) to within an average value of ± 20.4%. It is demonstrated that the reduced-order model’s predictions fall within 40% of the corresponding published values 95% of the time, and in the worst case, the associated discrepancy is 45.9%, suggesting that the accuracy of the machine learned model is indeed comparable to the TEAs that were used to build it. Moreover, the terms in the model are physically interpretable, conferring greater reliability to the use of its predictions. The model can be used to predict the dependence of MSFP on biocrude yield, scale, and feedstock cost; interestingly, MFSP is insensitive to biocrude yield and/or scale under many situations of interest and identifying the critical value for a given application is crucial to optimizing economic performance. The proposed model can be also extended to evaluate economic performance of newly developed HTL-based processes, including catalytic HTL, and the methodological framework used in this study is deemed appropriate for the development of machine learned TEA models in cases of other similar waste-to-energy technologies.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"24 ","pages":"Article 100756"},"PeriodicalIF":7.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142540339","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.2024.100788
Ajay Muraleedharan Nair , Christopher Wilson , Babak Kamkari , Simon Hodge , Ming Jun Huang , Philip Griffiths , Neil J Hewitt
This study presents the development and performance evaluation of an innovative thermal energy storage (TES) system utilizing a commercially available bioderived organic phase change material (PCM) for domestic hot water production. The primary objective of this research is to enhance the efficiency and effectiveness of thermal energy storage solutions by macro-encapsulating the PCM-expanded graphite (EG) compressed modules in a multi-pass tube arrangement. A comprehensive experimental setup was employed to investigate the thermal performance of the proposed TES unit, focusing on charging and discharging cycles. Key findings reveal that conduction is the dominant mode of heat transfer, with the system achieving a significant maximum average charging power of 1440 W and a discharging power of 1990 W. The thermal energy storage capacity reached an impressive 12.6 MJ, enabling the discharge of 90 % of stored energy within 90 min. Furthermore, the exergy analysis indicated high exergy efficiencies, with charging efficiencies reaching 98 % and overall exergy efficiency at 18 %.
The implications of this research are significant, demonstrating the feasibility of using bioderived organic PCM for sustainable energy applications. It highlights the potential of the modular structure of the system to integrate with heat pump and solar energy systems, thereby enhancing efficiency and sustainability in domestic hot water applications. This work significantly contributes to the advancement of sustainable thermal energy storage technologies and establishes a solid foundation for future studies aimed at optimizing TES systems for domestic hot water production.
{"title":"Energy and exergy analysis of a multipass macro-encapsulated phase change material/expanded graphite composite thermal energy storage for domestic hot water applications","authors":"Ajay Muraleedharan Nair , Christopher Wilson , Babak Kamkari , Simon Hodge , Ming Jun Huang , Philip Griffiths , Neil J Hewitt","doi":"10.1016/j.ecmx.2024.100788","DOIUrl":"10.1016/j.ecmx.2024.100788","url":null,"abstract":"<div><div>This study presents the development and performance evaluation of an innovative thermal energy storage (TES) system utilizing a commercially available bioderived organic phase change material (PCM) for domestic hot water production. The primary objective of this research is to enhance the efficiency and effectiveness of thermal energy storage solutions by macro-encapsulating the PCM-expanded graphite (EG) compressed modules in a multi-pass tube arrangement. A comprehensive experimental setup was employed to investigate the thermal performance of the proposed TES unit, focusing on charging and discharging cycles. Key findings reveal that conduction is the dominant mode of heat transfer, with the system achieving a significant maximum average charging power of 1440 W and a discharging power of 1990 W. The thermal energy storage capacity reached an impressive 12.6 MJ, enabling the discharge of 90 % of stored energy within 90 min. Furthermore, the exergy analysis indicated high exergy efficiencies, with charging efficiencies reaching 98 % and overall exergy efficiency at 18 %.</div><div>The implications of this research are significant, demonstrating the feasibility of using bioderived organic PCM for sustainable energy applications. It highlights the potential of the modular structure of the system to integrate with heat pump and solar energy systems, thereby enhancing efficiency and sustainability in domestic hot water applications. This work significantly contributes to the advancement of sustainable thermal energy storage technologies and establishes a solid foundation for future studies aimed at optimizing TES systems for domestic hot water production.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"24 ","pages":"Article 100788"},"PeriodicalIF":7.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663854","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.2024.100730
Lukas Koenemann , Astrid Bensmann , Johannes Gerster , Richard Hanke-Rauschenbach
Due to the availability of flexibility, Decentralized Energy Systems (DES) play a central role in integrating renewable energies. To efficiently utilize renewable energy, dispatchable components must be operated to bridge the time gap between inflexible supply and energy demand. Due to the large number of energy converters, energy storage systems, and flexible consumers, there are many ways to achieve this. Conventional rule-based dispatch strategies often reach their limits here, and optimized dispatch strategies (e.g., model predictive control or the optimal dispatch) are usually based on very good forecasts. Reinforcement learning, particularly the application of Artificial Neural Networks (ANN), offers the possibility to learn complex decision-making processes. Since long training times are required for this, an efficient training framework is needed. The present paper proposes different training methods to learn an ANN-based dispatch strategy. In Method I, the ANN attempts to learn the solution to the corresponding optimal dispatch problem. In method II, the energy system model is simulated during the training to compute the observation state and operating costs resulting from the ANN-based dispatch. Method III uses the fast executable Method I solution as a warm-up solution for the computationally expensive training with Method II. In the present paper, a model-based analysis compares the different ANN-based dispatch strategies with rule-based dispatch strategies, model predictive dispatch (MPC), and optimal dispatch regarding their computational efficiency and the resulting operating costs. The dispatch strategies are compared based on three case studies with different system topologies for which training and test data are applied.
Training method I proved to be non-competitive. However, training methods II and III significantly outperformed rule-based dispatch strategies across all case studies for both training and test data sets. Notably, methods II and III also surpassed MPC-based dispatch strategies under high and medium uncertainty forecasts for the training data in the first two case studies. In contrast, MPC-based dispatch was superior in the third case study, likely due to the higher system’s complexity and in the test data set due to the methodological advantage of being optimized for each specific data set. The effectiveness of training method III depends on the performance of the warm-up training with method I: warm-up is beneficial only if this results in an already promising dispatch (as seen in case study two). Otherwise, training method II proves more effective, as observed in case studies one and three.
由于具有灵活性，分散式能源系统（DES）在整合可再生能源方面发挥着核心作用。为了有效利用可再生能源，必须运行可调度组件，以弥补不灵活的供应与能源需求之间的时间差。由于存在大量的能量转换器、储能系统和灵活的用户，实现这一目标的方法有很多。传统的基于规则的调度策略往往在此达到极限，而优化的调度策略（如模型预测控制或最优调度）通常基于非常好的预测。强化学习，特别是人工神经网络（ANN）的应用，为学习复杂的决策过程提供了可能。由于这需要较长的训练时间，因此需要一个高效的训练框架。本文提出了不同的训练方法来学习基于 ANN 的调度策略。在方法 I 中，ANN 试图学习相应最优调度问题的解决方案。在方法 II 中，在训练过程中模拟能源系统模型，计算基于 ANN 的调度产生的观测状态和运行成本。方法 III 将可快速执行的方法 I 解决方案作为热身解决方案，用于计算成本高昂的方法 II 训练。在本文中，基于模型的分析比较了不同的基于 ANN 的调度策略与基于规则的调度策略、模型预测调度（MPC）以及最优调度的计算效率和由此产生的运营成本。调度策略的比较基于三个不同系统拓扑结构的案例研究，并应用了训练和测试数据。然而，在所有案例研究中，无论是训练数据集还是测试数据集，训练方法 II 和 III 都明显优于基于规则的调度策略。值得注意的是，在前两个案例研究的训练数据中，方法 II 和 III 在高不确定性预测和中不确定性预测下的表现也超过了基于 MPC 的调度策略。相比之下，基于 MPC 的调度策略在第三个案例研究中更胜一筹，这可能是由于系统的复杂性更高，而在测试数据集中则是由于针对每个特定数据集进行了优化的方法优势。训练方法 III 的有效性取决于使用方法 I 进行热身训练的效果：热身训练只有在调度效果已经很好的情况下才有益处（如案例研究二所示）。否则，正如案例研究一和三所示，训练方法 II 更为有效。
{"title":"Dispatch of decentralized energy systems using artificial neural networks: A comparative analysis with emphasis on training methods","authors":"Lukas Koenemann , Astrid Bensmann , Johannes Gerster , Richard Hanke-Rauschenbach","doi":"10.1016/j.ecmx.2024.100730","DOIUrl":"10.1016/j.ecmx.2024.100730","url":null,"abstract":"<div><div>Due to the availability of flexibility, Decentralized Energy Systems (DES) play a central role in integrating renewable energies. To efficiently utilize renewable energy, dispatchable components must be operated to bridge the time gap between inflexible supply and energy demand. Due to the large number of energy converters, energy storage systems, and flexible consumers, there are many ways to achieve this. Conventional rule-based dispatch strategies often reach their limits here, and optimized dispatch strategies (e.g., model predictive control or the optimal dispatch) are usually based on very good forecasts. Reinforcement learning, particularly the application of Artificial Neural Networks (ANN), offers the possibility to learn complex decision-making processes. Since long training times are required for this, an efficient training framework is needed. The present paper proposes different training methods to learn an ANN-based dispatch strategy. In Method I, the ANN attempts to learn the solution to the corresponding optimal dispatch problem. In method II, the energy system model is simulated during the training to compute the observation state and operating costs resulting from the ANN-based dispatch. Method III uses the fast executable Method I solution as a warm-up solution for the computationally expensive training with Method II. In the present paper, a model-based analysis compares the different ANN-based dispatch strategies with rule-based dispatch strategies, model predictive dispatch (MPC), and optimal dispatch regarding their computational efficiency and the resulting operating costs. The dispatch strategies are compared based on three case studies with different system topologies for which training and test data are applied.</div><div>Training method I proved to be non-competitive. However, training methods II and III significantly outperformed rule-based dispatch strategies across all case studies for both training and test data sets. Notably, methods II and III also surpassed MPC-based dispatch strategies under high and medium uncertainty forecasts for the training data in the first two case studies. In contrast, MPC-based dispatch was superior in the third case study, likely due to the higher system’s complexity and in the test data set due to the methodological advantage of being optimized for each specific data set. The effectiveness of training method III depends on the performance of the warm-up training with method I: warm-up is beneficial only if this results in an already promising dispatch (as seen in case study two). Otherwise, training method II proves more effective, as observed in case studies one and three.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"24 ","pages":"Article 100730"},"PeriodicalIF":7.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142437804","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.2024.100743
Marina Facci , Eloisa di Sipio , Gianluca Gola , Giordano Montegrossi , Antonio Galgaro
This study assesses the feasibility of repurposing abandoned oil and gas wells in Italy for geothermal energy production employing a geothermal closed-loop system. A systematic methodology was developed, beginning with raw data collection and progressing to numerical simulations using COMSOL Multiphysics to model a U-shaped deep closed-loop geothermal heat exchanger. The analysis relied on a public database of wells drilled in Italy since the mid-20th century. The Horner plot correction method was applied to measured temperature data to obtain accurate geothermal gradients across Italy, which were then used as input parameters for a numerical sensitivity analysis. The results highlight the critical role of the geothermal gradient and heat carrier fluid flow rate in determining system performance. Regions in Italy with geothermal gradients exceeding 40 °C/km, particularly in the Tyrrhenian area, were identified as having high potential for this technology. A preliminary analysis of a virtual Organic Rankine Cycle (ORC) system estimated power production of 73 kW, with an efficiency of 11.66 % after 25 years of operation under optimal conditions (5 l/s flow rate, 60 °C/km geothermal gradient, and 70 °C evaporation temperature).
{"title":"Sustainable reuse of oil and gas wells for geothermal energy production: Numerical analysis of deep closed loop solutions in Italy","authors":"Marina Facci , Eloisa di Sipio , Gianluca Gola , Giordano Montegrossi , Antonio Galgaro","doi":"10.1016/j.ecmx.2024.100743","DOIUrl":"10.1016/j.ecmx.2024.100743","url":null,"abstract":"<div><div>This study assesses the feasibility of repurposing abandoned oil and gas wells in Italy for geothermal energy production employing a geothermal closed-loop system. A systematic methodology was developed, beginning with raw data collection and progressing to numerical simulations using COMSOL Multiphysics to model a U-shaped deep closed-loop geothermal heat exchanger. The analysis relied on a public database of wells drilled in Italy since the mid-20th century. The Horner plot correction method was applied to measured temperature data to obtain accurate geothermal gradients across Italy, which were then used as input parameters for a numerical sensitivity analysis. The results highlight the critical role of the geothermal gradient and heat carrier fluid flow rate in determining system performance. Regions in Italy with geothermal gradients exceeding 40 °C/km, particularly in the Tyrrhenian area, were identified as having high potential for this technology. A preliminary analysis of a virtual Organic Rankine Cycle (ORC) system estimated power production of 73 kW, with an efficiency of 11.66 % after 25 years of operation under optimal conditions (5 l/s flow rate, 60 °C/km geothermal gradient, and 70 °C evaporation temperature).</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"24 ","pages":"Article 100743"},"PeriodicalIF":7.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445676","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}
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