Sustainable Energy Technologies and Assessments最新文献_第6页

A self-powered triboelectric wind detection sensor with adaptive electromagnetic damping adjusting mechanism

IF 7.1 2区工程技术 Q1 ENERGY & FUELS

Sustainable Energy Technologies and Assessments

Pub Date : 2025-01-01 DOI: 10.1016/j.seta.2024.104132

Yangdong Zuo , Jian Feng , Yanyan Gao , Yubao Li , Lingfei Qi

Wind energy as a primary clean and non-polluting renewable energy source has unlimited prospects for development and research. Some challenges limit the harvesting performance of wind energy, such as severe generator starting torque and high material wear. To solve these issues, this paper proposes a thrust-bearing-based triboelectric sensor detection actuation (TENS-DA) system for optimizing the starting torque of electromagnetic wind generator. The proposed detection actuation system consists of 3 components: a point-contact thrust-bearing type triboelectric nanosensor (TENS), the long-short-term memory (LSTM) network deep learning algorithm, and a self-regulating circuit, which reduces both the starting torque of the generator and the wear of the material. The system uses TENS as a sensitive sensor to acquire the outside wind condition in real-time, and after the LSTM network reasoning out the result. Then the Raspberry Pi adjusts the effective number of coils of Electromagnetic generator (EMG) according to the result to realize the real-time regulation of EMG starting torque. The experimental results show that the peak value of the TENS-DA system output power is 1.17 W at a wind speed of 8 m/s. Furthermore, the TENS-DA system is capable of harvesting wind energy with a low wind speed of 1.3 m/s. With a sample size is 6000, the TENS-DA system has a wind speed detection accuracy of 96.13 %, which can accurately detect external wind conditions. Finally, the TENS-DA system detects outside wind conditions in real-time and adaptively regulates the starting torque of the EMG. This optimization strategy will provide essential guidance and reference for wind energy harvesting.

{"title":"A self-powered triboelectric wind detection sensor with adaptive electromagnetic damping adjusting mechanism","authors":"Yangdong Zuo , Jian Feng , Yanyan Gao , Yubao Li , Lingfei Qi","doi":"10.1016/j.seta.2024.104132","DOIUrl":"10.1016/j.seta.2024.104132","url":null,"abstract":"<div><div>Wind energy as a primary clean and non-polluting renewable energy source has unlimited prospects for development and research. Some challenges limit the harvesting performance of wind energy, such as severe generator starting torque and high material wear. To solve these issues, this paper proposes a thrust-bearing-based triboelectric sensor detection actuation (TENS-DA) system for optimizing the starting torque of electromagnetic wind generator. The proposed detection actuation system consists of 3 components: a point-contact thrust-bearing type triboelectric nanosensor (TENS), the long-short-term memory (LSTM) network deep learning algorithm, and a self-regulating circuit, which reduces both the starting torque of the generator and the wear of the material. The system uses TENS as a sensitive sensor to acquire the outside wind condition in real-time, and after the LSTM network reasoning out the result. Then the Raspberry Pi adjusts the effective number of coils of Electromagnetic generator (EMG) according to the result to realize the real-time regulation of EMG starting torque. The experimental results show that the peak value of the TENS-DA system output power is 1.17 W at a wind speed of 8 m/s. Furthermore, the TENS-DA system is capable of harvesting wind energy with a low wind speed of 1.3 m/s. With a sample size is 6000, the TENS-DA system has a wind speed detection accuracy of 96.13 %, which can accurately detect external wind conditions. Finally, the TENS-DA system detects outside wind conditions in real-time and adaptively regulates the starting torque of the EMG. This optimization strategy will provide essential guidance and reference for wind energy harvesting.</div></div>","PeriodicalId":56019,"journal":{"name":"Sustainable Energy Technologies and Assessments","volume":"73 ","pages":"Article 104132"},"PeriodicalIF":7.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143162537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards a digitally enabled intelligent coal mine integrated energy system: Evolution, conceptualization, and implementation

IF 7.1 2区工程技术 Q1 ENERGY & FUELS

Sustainable Energy Technologies and Assessments

Pub Date : 2025-01-01 DOI: 10.1016/j.seta.2024.104128

Bo Zeng , Xinyu Yang , Pinduan Hu , Yuqing Wang , Houqi Dong , Dunwei Gong , Xianming Ye

The consensus on energy-saving and low-carbon goals led to greater requirements for the sustainable development of coal mines (CMs) worldwide. Against the above backdrop, the concept of Coal Mine Integrated Energy System (CMIES), a promising solution for coping with the smartization and decarbonization issues in the future development of CMs, has been proposed. However, the complexities of CM structure and operations, the shortage of automation and intelligent control techniques, and the stringent operation criteria, lead to high energy consumption, inefficient resource utilization, and unsatisfactory renewable energy accommodation in the mining system. Here, research on the emerging digital technologies and their potential applications is reviewed to provide reference for enhancing energy efficiency and mitigating emissions in future CM energy systems. First, the evolution of mining energy production and consumption paradigms is summarized from the perspective of technological advancements. Subsequently, the need to develop digitally enabled CMIES through a combined economic-security-emission analysis is discussed. To overcome the obstacles hindering the digital transformation of CM energy systems, a hierarchical implementation framework for digitally enabled CMIES involving the value, physical, and information layers is proposed. Furthermore, an intelligent information system incorporating device sensing and intelligence technology, new information network, big data, and digital twin is designed, and specific application scenarios are presented. Finally, the challenges and opportunities for future development of digitally enabled CMIES are discussed based on the Strengths-Weaknesses-Opportunities-Threats (SWOT) analysis.

{"title":"Towards a digitally enabled intelligent coal mine integrated energy system: Evolution, conceptualization, and implementation","authors":"Bo Zeng , Xinyu Yang , Pinduan Hu , Yuqing Wang , Houqi Dong , Dunwei Gong , Xianming Ye","doi":"10.1016/j.seta.2024.104128","DOIUrl":"10.1016/j.seta.2024.104128","url":null,"abstract":"<div><div>The consensus on energy-saving and low-carbon goals led to greater requirements for the sustainable development of coal mines (CMs) worldwide. Against the above backdrop, the concept of Coal Mine Integrated Energy System (CMIES), a promising solution for coping with the smartization and decarbonization issues in the future development of CMs, has been proposed. However, the complexities of CM structure and operations, the shortage of automation and intelligent control techniques, and the stringent operation criteria, lead to high energy consumption, inefficient resource utilization, and unsatisfactory renewable energy accommodation in the mining system. Here, research on the emerging digital technologies and their potential applications is reviewed to provide reference for enhancing energy efficiency and mitigating emissions in future CM energy systems. First, the evolution of mining energy production and consumption paradigms is summarized from the perspective of technological advancements. Subsequently, the need to develop digitally enabled CMIES through a combined economic-security-emission analysis is discussed. To overcome the obstacles hindering the digital transformation of CM energy systems, a hierarchical implementation framework for digitally enabled CMIES involving the value, physical, and information layers is proposed. Furthermore, an intelligent information system incorporating device sensing and intelligence technology, new information network, big data, and digital twin is designed, and specific application scenarios are presented. Finally, the challenges and opportunities for future development of digitally enabled CMIES are discussed based on the Strengths-Weaknesses-Opportunities-Threats (SWOT) analysis.</div></div>","PeriodicalId":56019,"journal":{"name":"Sustainable Energy Technologies and Assessments","volume":"73 ","pages":"Article 104128"},"PeriodicalIF":7.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143162565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing energy efficiency and profitability in microgrids through a genetic algorithm approach, analyzing the use of storage systems

IF 7.1 2区工程技术 Q1 ENERGY & FUELS

Sustainable Energy Technologies and Assessments

Pub Date : 2025-01-01 DOI: 10.1016/j.seta.2024.104154

Dácil Díaz-Bello , Carlos Vargas-Salgado , Tomás Gómez-Navarro , Jesús Águila-León

Due to intermittent, renewable energy systems struggle to meet demands efficiently and reliably. This research is rooted in photovoltaic systems, incorporating demand response optimization via genetic algorithms, generation forecasting using an artificial neural network, and integrating a storage system, looking for the optimal configuration to increase efficiency and system profitability. The investigation analyzes sunny and clouded seasons. Four scenarios are considered and compared to the baseline case. The baseline is the first scenario, which involves photovoltaics and a grid. In the second scenario, optimization focuses on photovoltaic generation using neural networks and incorporates demand response. The third scenario enhances the first one by introducing batteries. The fourth scenario refines the second one by including batteries. The simulations are developed using MATLAB, and an economic analysis utilizing HOMER is conducted. The findings reveal that the proposed artificial neural network exhibits superior root mean square errors at 443.62 W for photovoltaic forecasting, with an R-value of 0.9751. Applying the genetic algorithm results in a 15 % increase in self-consumption and a 52 % reduction in imported energy costs. The results showcase the model’s ability to minimize grid dependency and enhance efficiency. Economically, scenario two is the most favorable, achieving a payback of 3.6 years.

{"title":"Enhancing energy efficiency and profitability in microgrids through a genetic algorithm approach, analyzing the use of storage systems","authors":"Dácil Díaz-Bello , Carlos Vargas-Salgado , Tomás Gómez-Navarro , Jesús Águila-León","doi":"10.1016/j.seta.2024.104154","DOIUrl":"10.1016/j.seta.2024.104154","url":null,"abstract":"<div><div>Due to intermittent, renewable energy systems struggle to meet demands efficiently and reliably. This research is rooted in photovoltaic systems, incorporating demand response optimization via genetic algorithms, generation forecasting using an artificial neural network, and integrating a storage system, looking for the optimal configuration to increase efficiency and system profitability. The investigation analyzes sunny and clouded seasons. Four scenarios are considered and compared to the baseline case. The baseline is the first scenario, which involves photovoltaics and a grid. In the second scenario, optimization focuses on photovoltaic generation using neural networks and incorporates demand response. The third scenario enhances the first one by introducing batteries. The fourth scenario refines the second one by including batteries. The simulations are developed using MATLAB, and an economic analysis utilizing HOMER is conducted. The findings reveal that the proposed artificial neural network exhibits superior root mean square errors at 443.62 W for photovoltaic forecasting, with an R-value of 0.9751. Applying the genetic algorithm results in a 15 % increase in self-consumption and a 52 % reduction in imported energy costs. The results showcase the model’s ability to minimize grid dependency and enhance efficiency. Economically, scenario two is the most favorable, achieving a payback of 3.6 years.</div></div>","PeriodicalId":56019,"journal":{"name":"Sustainable Energy Technologies and Assessments","volume":"73 ","pages":"Article 104154"},"PeriodicalIF":7.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143162880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal conditions to maximize the fixed carbon content and the gas higher heating value of chrome-tanned leather shavings (CTLS) processed through fast pyrolysis

IF 7.1 2区工程技术 Q1 ENERGY & FUELS

Sustainable Energy Technologies and Assessments

Pub Date : 2025-01-01 DOI: 10.1016/j.seta.2024.104134

Edson Antonio Canôas , Marília Vasconcellos Agnesini , Cristina Filomêna Pereira Rosa Paschoalato , Murilo Daniel de Mello Innocentini

Chromium remains a major obstacle to environmentally friendly management of the massive amounts of leather waste produced by tanneries worldwide. Pyrolysis has been considered an interesting cost-benefit solution because the remaining char fixes carbon and the gases contain energy that can be reused in the process. Nevertheless, the optimal pyrolysis conditions must be determined to maximize carbon sequestration, minimize the volume of Cr-containing char and produce gases with the highest heating value. This study investigated the fast pyrolysis of chrome-tanned leather shavings (CTLS) containing 2.84 wt% Cr (dry basis) to determine the optimal conditions for industrial scaleup. The partitioning of CTLS into char, liquid, and gas was investigated at heating rates of 41–76 °C/min, temperatures of 400–800 °C, and residence times of 15–60 min. The best pyrolysis condition was 15 min at 400 °C, which produced 659 g_char/kg_CTLS, with 38 wt% fixed carbon. This condition also resulted in 106 g_gases/kg_CTLS, with 17.2 wt% light hydrocarbons (C1-C5), and an estimated higher heating value of 7.2 MJ/kg. The resulting chars had Cr content in the trivalent state (Cr³⁺) ranging from 4.3 to 5.2 wt%, and despite the high calorific value, they should not be used as fuel without a robust flue gas post-treatment system.

{"title":"Optimal conditions to maximize the fixed carbon content and the gas higher heating value of chrome-tanned leather shavings (CTLS) processed through fast pyrolysis","authors":"Edson Antonio Canôas , Marília Vasconcellos Agnesini , Cristina Filomêna Pereira Rosa Paschoalato , Murilo Daniel de Mello Innocentini","doi":"10.1016/j.seta.2024.104134","DOIUrl":"10.1016/j.seta.2024.104134","url":null,"abstract":"<div><div>Chromium remains a major obstacle to environmentally friendly management of the massive amounts of leather waste produced by tanneries worldwide. Pyrolysis has been considered an interesting cost-benefit solution because the remaining char fixes carbon and the gases contain energy that can be reused in the process. Nevertheless, the optimal pyrolysis conditions must be determined to maximize carbon sequestration, minimize the volume of Cr-containing char and produce gases with the highest heating value. This study investigated the fast pyrolysis of chrome-tanned leather shavings (CTLS) containing 2.84 wt% Cr (dry basis) to determine the optimal conditions for industrial scaleup. The partitioning of CTLS into char, liquid, and gas was investigated at heating rates of 41–76 °C/min, temperatures of 400–800 °C, and residence times of 15–60 min. The best pyrolysis condition was 15 min at 400 °C, which produced 659 g<sub>char</sub>/kg<sub>CTLS</sub>, with 38 wt% fixed carbon. This condition also resulted in 106 g<sub>gases</sub>/kg<sub>CTLS</sub>, with 17.2 wt% light hydrocarbons (C1-C5), and an estimated higher heating value of 7.2 MJ/kg. The resulting chars had Cr content in the trivalent state (Cr<sup>3+</sup>) ranging from 4.3 to 5.2 wt%, and despite the high calorific value, they should not be used as fuel without a robust flue gas post-treatment system.</div></div>","PeriodicalId":56019,"journal":{"name":"Sustainable Energy Technologies and Assessments","volume":"73 ","pages":"Article 104134"},"PeriodicalIF":7.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143163441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on remaining useful life prediction method for lithium-ion battery based on improved GA-ACO-BPNN optimization algorithm

IF 7.1 2区工程技术 Q1 ENERGY & FUELS

Sustainable Energy Technologies and Assessments

Pub Date : 2025-01-01 DOI: 10.1016/j.seta.2024.104142

Che Wang , Zhangyu Huang , Chengbo He , Xintao Lin , Chenyu Li , Jingde Huang

Lithium-ion battery is the core components of new energy vehicles, and their failures are closely related to the reliability status of new energy vehicles. From engineering practice, the reduced lifespan and performance of lithium-ion batteries can not only easily lead to damage to the power system, but also cause significant economic losses and endanger people’s safety. An improved GA-ACO-BPNN optimization algorithm is established to predict the remaining useful life (RUL) of lithium-ion batteries. This algorithm combines the technical advantages of genetic algorithm (GA) and ant colony optimization (ACO) to improve the back propagation neural network (BPNN) model. Specifically, the path selection mechanism based on the ACO and the updating of pheromones strengthens the search direction of the GA. Additionally, the out-of-bounds judgment ensures that the model parameters remain within a set range, keeping the prediction results in a reasonable space. The results of experiments show that the improved GA-ACO-BPNN optimization algorithm accelerates the convergence speed, fitting speed, and accuracy of the neural network. Compared with traditional models, the prediction accuracy has improved by 3.8%, and it performs well in evaluation indicators such as RMSE, MAE, and MAPE, with a decrease of 59.9%, 72.6%, and 80.1% respectively. It demonstrates stronger robustness in detecting and warning the RUL of the lithium-ion battery and has significant engineering practical value.

{"title":"Research on remaining useful life prediction method for lithium-ion battery based on improved GA-ACO-BPNN optimization algorithm","authors":"Che Wang , Zhangyu Huang , Chengbo He , Xintao Lin , Chenyu Li , Jingde Huang","doi":"10.1016/j.seta.2024.104142","DOIUrl":"10.1016/j.seta.2024.104142","url":null,"abstract":"<div><div>Lithium-ion battery is the core components of new energy vehicles, and their failures are closely related to the reliability status of new energy vehicles. From engineering practice, the reduced lifespan and performance of lithium-ion batteries can not only easily lead to damage to the power system, but also cause significant economic losses and endanger people’s safety. An improved GA-ACO-BPNN optimization algorithm is established to predict the remaining useful life (RUL) of lithium-ion batteries. This algorithm combines the technical advantages of genetic algorithm (GA) and ant colony optimization (ACO) to improve the back propagation neural network (BPNN) model. Specifically, the path selection mechanism based on the ACO and the updating of pheromones strengthens the search direction of the GA. Additionally, the out-of-bounds judgment ensures that the model parameters remain within a set range, keeping the prediction results in a reasonable space. The results of experiments show that the improved GA-ACO-BPNN optimization algorithm accelerates the convergence speed, fitting speed, and accuracy of the neural network. Compared with traditional models, the prediction accuracy has improved by 3.8%, and it performs well in evaluation indicators such as RMSE, MAE, and MAPE, with a decrease of 59.9%, 72.6%, and 80.1% respectively. It demonstrates stronger robustness in detecting and warning the RUL of the lithium-ion battery and has significant engineering practical value.</div></div>","PeriodicalId":56019,"journal":{"name":"Sustainable Energy Technologies and Assessments","volume":"73 ","pages":"Article 104142"},"PeriodicalIF":7.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143163477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Coordinated stochastic power system operation between TSO, renewable sources, energy storage and hydrogen energy equipped DSO, and HSO

IF 7.1 2区工程技术 Q1 ENERGY & FUELS

Sustainable Energy Technologies and Assessments

Pub Date : 2025-01-01 DOI: 10.1016/j.seta.2024.104143

Tayfur Gökçek , Ozan Erdinç , İbrahim Şengör

The proliferation of distributed energy and concerns about carbon emissions entail a multi-energy enriched coordinated operation in the electrical power system. In this paper, a coordinated stochastic operation between the Transmission System Operator (TSO), renewable sources, energy storage, the hydrogen (

H_{2}

) energy-equipped Distribution System Operator (DSO), and the Hydrogen System Operator (HSO) is proposed. The stochasticity in the decentralized hierarchical structure is modeled with the IEEE RTS 24-Bus System for the TSO, and 7-Bus and 9-Bus systems for the DSOs, taking into account the uncertainty in renewable energy generation. Furthermore, the HSO participates in the wholesale market as a price maker, and interacts with the DSOs for the lower

H_{2}

prices. The total operation cost has been reduced by 33.8% thanks to the Analytical Target Cascading-based distributed AC power flow model. In this context, it is shown that the integrated use of

H_{2}

energy and distributed resources under power system coordination can provide remarkable advantages to the transmission, distribution, and

H_{2}

gas systems.

{"title":"Coordinated stochastic power system operation between TSO, renewable sources, energy storage and hydrogen energy equipped DSO, and HSO","authors":"Tayfur Gökçek , Ozan Erdinç , İbrahim Şengör","doi":"10.1016/j.seta.2024.104143","DOIUrl":"10.1016/j.seta.2024.104143","url":null,"abstract":"<div><div>The proliferation of distributed energy and concerns about carbon emissions entail a multi-energy enriched coordinated operation in the electrical power system. In this paper, a coordinated stochastic operation between the Transmission System Operator (TSO), renewable sources, energy storage, the hydrogen (<span><math><msub><mrow><mi>H</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span>) energy-equipped Distribution System Operator (DSO), and the Hydrogen System Operator (HSO) is proposed. The stochasticity in the decentralized hierarchical structure is modeled with the IEEE RTS 24-Bus System for the TSO, and 7-Bus and 9-Bus systems for the DSOs, taking into account the uncertainty in renewable energy generation. Furthermore, the HSO participates in the wholesale market as a price maker, and interacts with the DSOs for the lower <span><math><msub><mrow><mi>H</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span> prices. The total operation cost has been reduced by 33.8% thanks to the Analytical Target Cascading-based distributed AC power flow model. In this context, it is shown that the integrated use of <span><math><msub><mrow><mi>H</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span> energy and distributed resources under power system coordination can provide remarkable advantages to the transmission, distribution, and <span><math><msub><mrow><mi>H</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span> gas systems.</div></div>","PeriodicalId":56019,"journal":{"name":"Sustainable Energy Technologies and Assessments","volume":"73 ","pages":"Article 104143"},"PeriodicalIF":7.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143163479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An inclusive policy design for Article 6 implementation of Paris agreement -A Taiwan practice

IF 7.1 2区工程技术 Q1 ENERGY & FUELS

Sustainable Energy Technologies and Assessments

Pub Date : 2025-01-01 DOI: 10.1016/j.seta.2024.104140

Chen-An Lien , Chien-Te Fan , Shih-Ming Chung , Wei-Chen Tsai , Wen-Cheng Hu

The response to worldwide climate emergencies materialized in the enactment of the Paris Agreement with inclusive global efforts on net-zero emissions. Thus far, the rulebook for Article 6 of the Paris Agreement contains only broad principles, while infrastructure is still being developed, particularly for various purposes, including Other International Mitigation Purposes. This study reviews contemporary Nationally Determined Contributions (NDCs) policies in major and emerging economies, specifically those implementing carbon pricing and crediting practices. Given that the climate legislation of Taiwan achieved net-zero emissions, set a 2030 NDC target, and enforced carbon regulations, the Taiwan Crediting Integration Framework was introduced as a feasible research example, with an inclusive policy design, including existing carbon pricing instrument applications to carbon fees and potential emission trading systems to carbon credit, derived from the Paris Mechanisms. We found that with a 10 percent International Transferred Mitigation Outcomes offset to 2005 levels, the potential opportunity costs and marginal cost savings could benefit approximately US$ 737 M in 2030 when conducting the NDC target of Taiwan. The resulting carbon pricing governance in Taiwan will implement carbon pricing and be strictly bound to absolute emission reduction targets, ensuring no deviation from the net-zero pathway and preventing greenwashing.

{"title":"An inclusive policy design for Article 6 implementation of Paris agreement -A Taiwan practice","authors":"Chen-An Lien , Chien-Te Fan , Shih-Ming Chung , Wei-Chen Tsai , Wen-Cheng Hu","doi":"10.1016/j.seta.2024.104140","DOIUrl":"10.1016/j.seta.2024.104140","url":null,"abstract":"<div><div>The response to worldwide climate emergencies materialized in the enactment of the Paris Agreement with inclusive global efforts on net-zero emissions. Thus far, the rulebook for Article 6 of the Paris Agreement contains only broad principles, while infrastructure is still being developed, particularly for various purposes, including Other International Mitigation Purposes. This study reviews contemporary Nationally Determined Contributions (NDCs) policies in major and emerging economies, specifically those implementing carbon pricing and crediting practices. Given that the climate legislation of Taiwan achieved net-zero emissions, set a 2030 NDC target, and enforced carbon regulations, the Taiwan Crediting Integration Framework was introduced as a feasible research example, with an inclusive policy design, including existing carbon pricing instrument applications to carbon fees and potential emission trading systems to carbon credit, derived from the Paris Mechanisms. We found that with a 10 percent International Transferred Mitigation Outcomes offset to 2005 levels, the potential opportunity costs and marginal cost savings could benefit approximately US$ 737 M in 2030 when conducting the NDC target of Taiwan. The resulting carbon pricing governance in Taiwan will implement carbon pricing and be strictly bound to absolute emission reduction targets, ensuring no deviation from the net-zero pathway and preventing greenwashing.</div></div>","PeriodicalId":56019,"journal":{"name":"Sustainable Energy Technologies and Assessments","volume":"73 ","pages":"Article 104140"},"PeriodicalIF":7.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143162563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Assessment of wind hazard at wind turbine sites based on CFD simulation under tropical cyclone conditions

IF 7.1 2区工程技术 Q1 ENERGY & FUELS

Sustainable Energy Technologies and Assessments

Pub Date : 2025-01-01 DOI: 10.1016/j.seta.2024.104109

Yuhui Li , Shengming Tang , Xiaodong Zhang , Hui Yu , Rong Zhu , Limin Zhou

In this study, a new TC wind hazard assessment framework at wind turbine sites was established based on a newly developed CFD model that accounts for the rotational effects of the mesoscale wind field in the TC boundary layer. The CFD-simulated wind field was proved to agree well with the nacelle measurements within the wind farm with a correlation coefficient above 0.85. Then, a modified composite risk index (CRI) was proposed by incorporating turbulence intensity (I). The CRI at wind turbine sites was found to be primarily controlled by horizontal wind speed (V) and I. When the TC center was close to the wind farm (< 65 km), V significantly increased and contributed more than 70 % of CRI as a dominant factor. Comparatively, when the TC center was far away and V was relatively small, I emerged as the dominant factor and contributed over 60 % of CRI. In addition, hazard factors related to changes in wind direction accounted for 10–20 % of CRI during the impact of TC, highlighting their importance as non-negligible hazard factors. This hazard assessment method is expected to provide a reference for wind farm microsite and wind turbine selection under TC conditions.

{"title":"Assessment of wind hazard at wind turbine sites based on CFD simulation under tropical cyclone conditions","authors":"Yuhui Li , Shengming Tang , Xiaodong Zhang , Hui Yu , Rong Zhu , Limin Zhou","doi":"10.1016/j.seta.2024.104109","DOIUrl":"10.1016/j.seta.2024.104109","url":null,"abstract":"<div><div>In this study, a new TC wind hazard assessment framework at wind turbine sites was established based on a newly developed CFD model that accounts for the rotational effects of the mesoscale wind field in the TC boundary layer. The CFD-simulated wind field was proved to agree well with the nacelle measurements within the wind farm with a correlation coefficient above 0.85. Then, a modified composite risk index (CRI) was proposed by incorporating turbulence intensity (<em>I</em>). The CRI at wind turbine sites was found to be primarily controlled by horizontal wind speed (<em>V</em>) and <em>I</em>. When the TC center was close to the wind farm (< 65 km), <em>V</em> significantly increased and contributed more than 70 % of CRI as a dominant factor. Comparatively, when the TC center was far away and <em>V</em> was relatively small, <em>I</em> emerged as the dominant factor and contributed over 60 % of CRI. In addition, hazard factors related to changes in wind direction accounted for 10–20 % of CRI during the impact of TC, highlighting their importance as non-negligible hazard factors. This hazard assessment method is expected to provide a reference for wind farm microsite and wind turbine selection under TC conditions.</div></div>","PeriodicalId":56019,"journal":{"name":"Sustainable Energy Technologies and Assessments","volume":"73 ","pages":"Article 104109"},"PeriodicalIF":7.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143163437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Experimental investigation of the soiling impact on the generation of a photovoltaic plant in an urban area of the Brazilian semiarid

IF 7.1 2区工程技术 Q1 ENERGY & FUELS

Sustainable Energy Technologies and Assessments

Pub Date : 2025-01-01 DOI: 10.1016/j.seta.2024.104153

José Janiere S. de Souza , Paulo C.M. Carvalho

The global expansion of photovoltaic (PV) systems in urban areas has accelerated. However, urban environments present unique challenges, including soiling, which significantly reduces PV module performance, especially in semiarid regions. This study investigates the impact of manual cleaning (MC) at varying frequencies and under different rainfall regimes on the performance of a PV plant (LEA2) in an urban area of the Brazilian semiarid. The results reveal a weekly normalized percentage relative difference in generation (%nE_LEA2) of up to 2.59 %, when repair works were carried out on a major avenue near LEA2, highlighting that anthropological actions cannot be neglected. An %nE_LEA2 increase of up to 27.93 % (from 0.93 % to 1.29 %) is observed when comparing the rainy with the pre-rainy season, whose average intervals between cleanings are 17.50 and 12.60 days, respectively. Furthermore, while rainfall can partially mitigate soiling, it is often insufficient to restore the PV plant performance without MC, particularly in the case of bird droppings. These findings offer valuable insights for optimizing PV system performance and provide a basis for developing customized maintenance strategies.

{"title":"Experimental investigation of the soiling impact on the generation of a photovoltaic plant in an urban area of the Brazilian semiarid","authors":"José Janiere S. de Souza , Paulo C.M. Carvalho","doi":"10.1016/j.seta.2024.104153","DOIUrl":"10.1016/j.seta.2024.104153","url":null,"abstract":"<div><div>The global expansion of photovoltaic (PV) systems in urban areas has accelerated. However, urban environments present unique challenges, including soiling, which significantly reduces PV module performance, especially in semiarid regions. This study investigates the impact of manual cleaning (MC) at varying frequencies and under different rainfall regimes on the performance of a PV plant (LEA2) in an urban area of the Brazilian semiarid. The results reveal a weekly normalized percentage relative difference in generation (%nE<sub>LEA2</sub>) of up to 2.59 %, when repair works were carried out on a major avenue near LEA2, highlighting that anthropological actions cannot be neglected. An %nE<sub>LEA2</sub> increase of up to 27.93 % (from 0.93 % to 1.29 %) is observed when comparing the rainy with the pre-rainy season, whose average intervals between cleanings are 17.50 and 12.60 days, respectively. Furthermore, while rainfall can partially mitigate soiling, it is often insufficient to restore the PV plant performance without MC, particularly in the case of bird droppings. These findings offer valuable insights for optimizing PV system performance and provide a basis for developing customized maintenance strategies.</div></div>","PeriodicalId":56019,"journal":{"name":"Sustainable Energy Technologies and Assessments","volume":"73 ","pages":"Article 104153"},"PeriodicalIF":7.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143163471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancement in power-to-methanol integration with steel industry waste gas utilization through solid oxide electrolyzer cells: Surrogate model-based approach for optimization

IF 7.1 2区工程技术 Q1 ENERGY & FUELS

Sustainable Energy Technologies and Assessments

Pub Date : 2025-01-01 DOI: 10.1016/j.seta.2024.104160

Ahmad Syauqi , Vijay Mohan Nagulapati , Yus Donald Chaniago , Juli Ayu Ningtyas , Riezqa Andika , Hankwon Lim

This study introduces an innovative approach using solid oxide electrolysis cells (SOEC) to co-electrolyze CO₂ and H₂O from steel industry emissions, converting them into syngas for methanol synthesis. To optimize this process, a surrogate model-based deep neural network (DNN) is employed. The process simulation result shows strong agreement between the model and experimental data, validated by polarization curves and product comparisons, with low RMSE values indicating its validity for generating data in subsequent processes. The DNN surrogate model accurately predicted key performance metrics, with high R² values for methanol production and power consumption, demonstrating its capability as a surrogate model for process simulation and use for further optimization. Optimization revealed that the ideal conditions for methanol synthesis occur at high temperatures, with low current density and steam flow. Additionally, the surrogate-based optimization method reduced computational time by a factor of 20. The use of SOEC dramatically enhanced methanol production, achieving nearly 10 times the productivity of systems without SOEC integration. This improvement also led to a substantial reduction in CO₂ emissions intensity, with the plant predicted to produce near-zero carbon emissions due to increased production efficiency and CO₂ utilization.

{"title":"Advancement in power-to-methanol integration with steel industry waste gas utilization through solid oxide electrolyzer cells: Surrogate model-based approach for optimization","authors":"Ahmad Syauqi , Vijay Mohan Nagulapati , Yus Donald Chaniago , Juli Ayu Ningtyas , Riezqa Andika , Hankwon Lim","doi":"10.1016/j.seta.2024.104160","DOIUrl":"10.1016/j.seta.2024.104160","url":null,"abstract":"<div><div>This study introduces an innovative approach using solid oxide electrolysis cells (SOEC) to co-electrolyze CO<sub>2</sub> and H<sub>2</sub>O from steel industry emissions, converting them into syngas for methanol synthesis. To optimize this process, a surrogate model-based deep neural network (DNN) is employed. The process simulation result shows strong agreement between the model and experimental data, validated by polarization curves and product comparisons, with low RMSE values indicating its validity for generating data in subsequent processes. The DNN surrogate model accurately predicted key performance metrics, with high R<sup>2</sup> values for methanol production and power consumption, demonstrating its capability as a surrogate model for process simulation and use for further optimization. Optimization revealed that the ideal conditions for methanol synthesis occur at high temperatures, with low current density and steam flow. Additionally, the surrogate-based optimization method reduced computational time by a factor of 20. The use of SOEC dramatically enhanced methanol production, achieving nearly 10 times the productivity of systems without SOEC integration. This improvement also led to a substantial reduction in CO<sub>2</sub> emissions intensity, with the plant predicted to produce near-zero carbon emissions due to increased production efficiency and CO<sub>2</sub> utilization.</div></div>","PeriodicalId":56019,"journal":{"name":"Sustainable Energy Technologies and Assessments","volume":"73 ","pages":"Article 104160"},"PeriodicalIF":7.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143163478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0