Pub Date : 2024-11-16DOI: 10.1016/j.renene.2024.121926
Mengyan Liu , Benfeng Zhu , Na Chen , Jie Zhu , Caihe Lei , Ruopeng Li , Yumeng Yang , Jiao Liu , Zhao Zhang , Peixia Yang , Oleg Levin , Elena Alekseeva , Bo Fang , Guoying Wei , Jingjing Yang
Solar energy is an eco-conscious substitute, for solar energy absorption and subsequent light-to-heat conversion, light-absorbing materials require broad-spectrum light absorption capabilities. Herein, we present the fabrication of broadband light-absorbing polypyrrole-carboxylated carbon nanotube membranes via a facile electrochemical deposition route. By manipulating electrochemical deposition time, the structure of the membranes was tailored, resulting in enhanced absorption, achieving over 98.95 % across the entire solar spectrum. The membranes demonstrated exemplary thermal efficacy and insensitivity to incident angles in photothermal conversion, the membranes facilitated a notable 12 °C temperature elevation within a simulated greenhouse compared to ambient conditions. Thus, these membranes exhibit considerable potential for widespread application in photothermal conversion and greenhouse technology.
{"title":"Broadband efficient light-absorbing SS-PPy@CNT membranes prepared by electrochemical deposition for photothermal conversion","authors":"Mengyan Liu , Benfeng Zhu , Na Chen , Jie Zhu , Caihe Lei , Ruopeng Li , Yumeng Yang , Jiao Liu , Zhao Zhang , Peixia Yang , Oleg Levin , Elena Alekseeva , Bo Fang , Guoying Wei , Jingjing Yang","doi":"10.1016/j.renene.2024.121926","DOIUrl":"10.1016/j.renene.2024.121926","url":null,"abstract":"<div><div>Solar energy is an eco-conscious substitute, for solar energy absorption and subsequent light-to-heat conversion, light-absorbing materials require broad-spectrum light absorption capabilities. Herein, we present the fabrication of broadband light-absorbing polypyrrole-carboxylated carbon nanotube membranes via a facile electrochemical deposition route. By manipulating electrochemical deposition time, the structure of the membranes was tailored, resulting in enhanced absorption, achieving over 98.95 % across the entire solar spectrum. The membranes demonstrated exemplary thermal efficacy and insensitivity to incident angles in photothermal conversion, the membranes facilitated a notable 12 °C temperature elevation within a simulated greenhouse compared to ambient conditions. Thus, these membranes exhibit considerable potential for widespread application in photothermal conversion and greenhouse technology.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"237 ","pages":"Article 121926"},"PeriodicalIF":9.0,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662864","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.renene.2024.121858
Zhenhua Xia , Guosheng Jia , Zeyu Tao , Wei Jia , Yishu Shi , Liwen Jin
High-temperature geothermal resources are increasingly being explored as an alternative to coal and natural gas for space heating. In light of the growing demand for energy conservation and emission reduction, it is crucial to conduct a comprehensive evaluation of geothermal energy according to environmental impact and energy cost. Based on geothermal heating systems in Xi'an, China, this study develops a model involving the life cycle assessment (LCA) method to assess the borehole heat exchanger (BHE) in heating systems, encompassing carbon intensity (Cintensity). The response surface method was employed to optimize the drilling depth, operating flow, and pipe diameter ratio, which influences carbon intensity. The findings revealed that the minimum Cintensity is 24.11 g(CO2)·kWh−1, corresponding to a 4000 m burial depth, 20 m3 h−1 flow rate, and 0.54 diameter ratio. However, these results diverge from the minimum levelized cost of energy (LCOE) of $7.84/GJ, with 3912.8 m, 34.32 m3 h−1, and 0.64. A dual-objective optimization indicates the allocation of weights to the objective functions influences the optimal drilling depth (the optimal value is between 3920.51 m and 3921.62 m) and operating flow rate (ranging from 28.8 to 31.4 m3 h−1). The optimal outcomes for LCOE and Cintensity are contingent upon the decision-makers' weight allocation.
{"title":"Multi-objective optimization of geothermal heating systems based on thermal economy and environmental impact evaluation","authors":"Zhenhua Xia , Guosheng Jia , Zeyu Tao , Wei Jia , Yishu Shi , Liwen Jin","doi":"10.1016/j.renene.2024.121858","DOIUrl":"10.1016/j.renene.2024.121858","url":null,"abstract":"<div><div>High-temperature geothermal resources are increasingly being explored as an alternative to coal and natural gas for space heating. In light of the growing demand for energy conservation and emission reduction, it is crucial to conduct a comprehensive evaluation of geothermal energy according to environmental impact and energy cost. Based on geothermal heating systems in Xi'an, China, this study develops a model involving the life cycle assessment (LCA) method to assess the borehole heat exchanger (BHE) in heating systems, encompassing carbon intensity (C<sub>intensity</sub>). The response surface method was employed to optimize the drilling depth, operating flow, and pipe diameter ratio, which influences carbon intensity. The findings revealed that the minimum C<sub>intensity</sub> is 24.11 g(CO<sub>2</sub>)·kWh<sup>−1</sup>, corresponding to a 4000 m burial depth, 20 m<sup>3</sup> h<sup>−1</sup> flow rate, and 0.54 diameter ratio. However, these results diverge from the minimum levelized cost of energy (LCOE) of $7.84/GJ, with 3912.8 m, 34.32 m<sup>3</sup> h<sup>−1</sup>, and 0.64. A dual-objective optimization indicates the allocation of weights to the objective functions influences the optimal drilling depth (the optimal value is between 3920.51 m and 3921.62 m) and operating flow rate (ranging from 28.8 to 31.4 m<sup>3</sup> h<sup>−1</sup>). The optimal outcomes for LCOE and C<sub>intensity</sub> are contingent upon the decision-makers' weight allocation.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"237 ","pages":"Article 121858"},"PeriodicalIF":9.0,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662811","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.renene.2024.121829
Qian Huang , Qing Feng
Coal-fired power generation is one of the major contributors to global carbon emissions. Biomass co-firing with coal is a cost-effective approach for carbon abatement. This paper formulates a hybrid carbon pricing initiative that combines an emission trading system (ETS) and a carbon tax, with ETS being applied to large power plants and carbon tax being applied to small power plants. A bi-level multi-objective optimization model is established to assist the multiple stakeholders to develop optimal strategies, and quantify environmental and economic impacts. In the proposed model, decision sequence from the authority to the coal-fired power plants is considered, and trade-offs between social welfare and carbon intensity is determined. Bi-level interactive fuzzy approach is adopted to search for satisfactory solutions. A case study is conducted to demonstrate the model’s practicality and efficiency, and the results reveal that this model is adequate for finding Stackelberg equilibrium between the hierarchical decision-makers with conflicting objectives. Sensitivity analyses are conducted to provide the stakeholders with reasonable and practical strategies under various situations. It is found that higher preferences for economic benefits and satisfactory degrees would increase carbon tax price. Management recommendations are provided to support the hybrid initiative for biomass co-firing with coal.
{"title":"Bi-level multi-objective optimization for a hybrid carbon pricing initiative towards biomass co-firing with coal","authors":"Qian Huang , Qing Feng","doi":"10.1016/j.renene.2024.121829","DOIUrl":"10.1016/j.renene.2024.121829","url":null,"abstract":"<div><div>Coal-fired power generation is one of the major contributors to global carbon emissions. Biomass co-firing with coal is a cost-effective approach for carbon abatement. This paper formulates a hybrid carbon pricing initiative that combines an emission trading system (ETS) and a carbon tax, with ETS being applied to large power plants and carbon tax being applied to small power plants. A bi-level multi-objective optimization model is established to assist the multiple stakeholders to develop optimal strategies, and quantify environmental and economic impacts. In the proposed model, decision sequence from the authority to the coal-fired power plants is considered, and trade-offs between social welfare and carbon intensity is determined. Bi-level interactive fuzzy approach is adopted to search for satisfactory solutions. A case study is conducted to demonstrate the model’s practicality and efficiency, and the results reveal that this model is adequate for finding Stackelberg equilibrium between the hierarchical decision-makers with conflicting objectives. Sensitivity analyses are conducted to provide the stakeholders with reasonable and practical strategies under various situations. It is found that higher preferences for economic benefits and satisfactory degrees would increase carbon tax price. Management recommendations are provided to support the hybrid initiative for biomass co-firing with coal.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"237 ","pages":"Article 121829"},"PeriodicalIF":9.0,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663233","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.renene.2024.121900
Muhammad Salman , Guimei Wang
Energy poverty in the Organisation for Economic Co-operation and Development (OECD) countries is a complex issue that needs careful and targeted solutions. Tens of millions of households in OECD countries struggle to pay for enough electricity and heating to meet their basic needs. Thus, it is essential to make a detailed assessment of rural energy poverty with an integrated approach. In so doing, we first measure rural energy poverty composite index across 38 OECD countries and their 5 major partners from 2000 to 2021. We then explored the linear effect of renewable energy technologies on energy poverty alleviation. Moreover, the mediating effect of green taxes on rural energy poverty through renewable energy technologies was analysed. Finally, the fuzzy regression discontinuity (FRD) method is employed to address the endogeneity issue associated with the assignment of the 2030 United Nations (UN) Agenda and to identify treatment effects. The results show that rural energy poverty improves over time. However, significant spatiotemporal disparities in alleviation efforts are found across countries. The results of Method of Moment Quantile Regression (MMQR) approach reveal that renewable energy technologies can accelerate rural energy poverty mitigation in the sample countries. We find that green taxation is an important mediating channel through which the adoption of renewable energy technologies alleviates rural energy poverty. The results of the Fuzzy-RD show that the 2030 UN agenda was effective in increasing the likelihood of rural energy poverty mitigation. The results remain consistent after a battery of robustness tests. This study offers valuable insights that can aid policymakers in formulating robust public policies.
{"title":"Rural energy poverty alleviation in OECD nations: An integrated analysis of renewable energy, green taxation, and the United Nations agenda 2030","authors":"Muhammad Salman , Guimei Wang","doi":"10.1016/j.renene.2024.121900","DOIUrl":"10.1016/j.renene.2024.121900","url":null,"abstract":"<div><div>Energy poverty in the Organisation for Economic Co-operation and Development (OECD) countries is a complex issue that needs careful and targeted solutions. Tens of millions of households in OECD countries struggle to pay for enough electricity and heating to meet their basic needs. Thus, it is essential to make a detailed assessment of rural energy poverty with an integrated approach. In so doing, we first measure rural energy poverty composite index across 38 OECD countries and their 5 major partners from 2000 to 2021. We then explored the linear effect of renewable energy technologies on energy poverty alleviation. Moreover, the mediating effect of green taxes on rural energy poverty through renewable energy technologies was analysed. Finally, the fuzzy regression discontinuity (FRD) method is employed to address the endogeneity issue associated with the assignment of the 2030 United Nations (UN) Agenda and to identify treatment effects. The results show that rural energy poverty improves over time. However, significant spatiotemporal disparities in alleviation efforts are found across countries. The results of Method of Moment Quantile Regression (MMQR) approach reveal that renewable energy technologies can accelerate rural energy poverty mitigation in the sample countries. We find that green taxation is an important mediating channel through which the adoption of renewable energy technologies alleviates rural energy poverty. The results of the Fuzzy-RD show that the 2030 UN agenda was effective in increasing the likelihood of rural energy poverty mitigation. The results remain consistent after a battery of robustness tests. This study offers valuable insights that can aid policymakers in formulating robust public policies.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"237 ","pages":"Article 121900"},"PeriodicalIF":9.0,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663142","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.renene.2024.121903
Shuaijun Xu , Baifeng Ji , Fan Xu , Changkun Chen
With the global demand for renewable energy rising, offshore renewable energy development has gained more attention. The combination of wind and wave energy is a new trend. Ensuring stability in combined systems is crucial for efficiency of absorbing energy. A novel combined wind and wave energy system with a semi-submersible floating wind turbine (FWT) and an array of six torus-shaped point absorber wave energy converters (WECs) is proposed. The dynamic response of combined system is investigated using 3D potential flow theory by comparing to the original system. The effects of power take-off (PTO) damping, WEC float shape and seasonal variation on the dynamic response and power performance of combined system are studied. The results show that the addition of WEC array improves the stability and power production of combined system. Meanwhile, the total power of combined system is approximately 2.5%–6.5 % higher than that of original system. PTO damping mainly affects the heave motion of combined system. As PTO damping increases, the first peak of mean power of WEC array shifts towards the long period, while the second peak of that shifts towards the short period. The conical-bottom WEC generates the most power compared to the flat-bottom WEC, hemispherical-bottom WEC and concave-bottom WEC. The combined system generates the most power in winter, and the total annual electricity output can be up to 2.99 × 104 MWh.
{"title":"Dynamic response and power performance of a combined semi-submersible floating wind turbine and point absorber wave energy converter array","authors":"Shuaijun Xu , Baifeng Ji , Fan Xu , Changkun Chen","doi":"10.1016/j.renene.2024.121903","DOIUrl":"10.1016/j.renene.2024.121903","url":null,"abstract":"<div><div>With the global demand for renewable energy rising, offshore renewable energy development has gained more attention. The combination of wind and wave energy is a new trend. Ensuring stability in combined systems is crucial for efficiency of absorbing energy. A novel combined wind and wave energy system with a semi-submersible floating wind turbine (FWT) and an array of six torus-shaped point absorber wave energy converters (WECs) is proposed. The dynamic response of combined system is investigated using 3D potential flow theory by comparing to the original system. The effects of power take-off (PTO) damping, WEC float shape and seasonal variation on the dynamic response and power performance of combined system are studied. The results show that the addition of WEC array improves the stability and power production of combined system. Meanwhile, the total power of combined system is approximately 2.5%–6.5 % higher than that of original system. PTO damping mainly affects the heave motion of combined system. As PTO damping increases, the first peak of mean power of WEC array shifts towards the long period, while the second peak of that shifts towards the short period. The conical-bottom WEC generates the most power compared to the flat-bottom WEC, hemispherical-bottom WEC and concave-bottom WEC. The combined system generates the most power in winter, and the total annual electricity output can be up to 2.99 × 10<sup>4</sup> MWh.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"237 ","pages":"Article 121903"},"PeriodicalIF":9.0,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662810","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.renene.2024.121888
Mustafa Kamal Pasha , Lingmei Dai , Dehua Liu , Wei Du , Miao Guo
Biodiesel yield prediction is vital for optimizing process efficiency, minimizing costs, and maintaining product quality. Traditional methods are labor-intensive, costly, and lack real-time capabilities, leading to inefficiencies in operations. Data-driven soft sensors offer real-time prediction but require extensive, high-quality datasets, posing practical challenges. To address these limitations, this study proposes a hybrid soft sensor model that integrates mechanistic and data-driven approaches. Mechanistic models were utilized to generate computational data via MATLAB®, reducing the reliance on costly laboratory experiments. A comprehensive dataset (n = 1500) comprising seven input variables—catalyst type, feedstock type, temperature, reaction time, free fatty acid (FFA) content, water content, and methanol-to-oil ratio—along with one output variable (biodiesel yield) was developed. This dataset was used to train various machine learning algorithms, with the artificial neural network (ANN) model demonstrating the highest predictive accuracy, achieving an R2 (goodness of fit) of 0.998 and root mean square error (RMSE) of 0.303. Hyperparameter tuning further enhanced the model's performance, reducing RMSE and the mean absolute error (MAE) by 63 % and 61.7 %, respectively. By combining mechanistic and data-driven techniques, this hybrid model effectively overcomes the limitations of traditional and purely data-driven methods, providing a cost-effective and efficient solution for biodiesel yield prediction and data generation.
{"title":"A hybrid soft sensor framework for real-time biodiesel yield prediction: Integrating mechanistic models and machine learning algorithms","authors":"Mustafa Kamal Pasha , Lingmei Dai , Dehua Liu , Wei Du , Miao Guo","doi":"10.1016/j.renene.2024.121888","DOIUrl":"10.1016/j.renene.2024.121888","url":null,"abstract":"<div><div>Biodiesel yield prediction is vital for optimizing process efficiency, minimizing costs, and maintaining product quality. Traditional methods are labor-intensive, costly, and lack real-time capabilities, leading to inefficiencies in operations. Data-driven soft sensors offer real-time prediction but require extensive, high-quality datasets, posing practical challenges. To address these limitations, this study proposes a hybrid soft sensor model that integrates mechanistic and data-driven approaches. Mechanistic models were utilized to generate computational data via MATLAB®, reducing the reliance on costly laboratory experiments. A comprehensive dataset (n = 1500) comprising seven input variables—catalyst type, feedstock type, temperature, reaction time, free fatty acid (FFA) content, water content, and methanol-to-oil ratio—along with one output variable (biodiesel yield) was developed. This dataset was used to train various machine learning algorithms, with the artificial neural network (ANN) model demonstrating the highest predictive accuracy, achieving an R<sup>2</sup> (goodness of fit) of 0.998 and root mean square error (RMSE) of 0.303. Hyperparameter tuning further enhanced the model's performance, reducing RMSE and the mean absolute error (MAE) by 63 % and 61.7 %, respectively. By combining mechanistic and data-driven techniques, this hybrid model effectively overcomes the limitations of traditional and purely data-driven methods, providing a cost-effective and efficient solution for biodiesel yield prediction and data generation.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"237 ","pages":"Article 121888"},"PeriodicalIF":9.0,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663239","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.renene.2024.121896
Wei Su , Zhengtao Ai , Bin Yang
Given the lack of consensus on the selection and design of appropriate latent heat storage exchangers (LHSEs) for practical applications, this study presents a framework for evaluating the performance of various LHSEs and a novel prediction model without involving complex differential equation systems is proposed to quickly predict the performance of LHSEs. The prediction accuracy is guaranteed by comparing against the validated numerical simulations under different geometries, inlet heat transfer fluid (HTF) parameters, and PCM properties. The proposed model performs well in predicting the LHSE performance under different geometries, inlet HTF parameters, and PCM properties. The maximum prediction errors for the effective operating time and air outlet temperature are 0.9 h and 1.9 °C, respectively. It implies that the proposed model has the potential to predict the performance of the LHSE under various conditions. Due to ignoring the temperature gradient within the PCM containers and the sensible thermal energy storage of the PCM, the predicted average PCM temperature is slightly overestimated during the first half and underestimated during the second half of the melting process. This study is anticipated to provide a new solution for performance evaluation and fast prediction of LHSEs.
{"title":"Performance of latent heat storage exchangers: Evaluation framework and fast prediction model","authors":"Wei Su , Zhengtao Ai , Bin Yang","doi":"10.1016/j.renene.2024.121896","DOIUrl":"10.1016/j.renene.2024.121896","url":null,"abstract":"<div><div>Given the lack of consensus on the selection and design of appropriate latent heat storage exchangers (LHSEs) for practical applications, this study presents a framework for evaluating the performance of various LHSEs and a novel prediction model without involving complex differential equation systems is proposed to quickly predict the performance of LHSEs. The prediction accuracy is guaranteed by comparing against the validated numerical simulations under different geometries, inlet heat transfer fluid (HTF) parameters, and PCM properties. The proposed model performs well in predicting the LHSE performance under different geometries, inlet HTF parameters, and PCM properties. The maximum prediction errors for the effective operating time and air outlet temperature are 0.9 h and 1.9 °C, respectively. It implies that the proposed model has the potential to predict the performance of the LHSE under various conditions. Due to ignoring the temperature gradient within the PCM containers and the sensible thermal energy storage of the PCM, the predicted average PCM temperature is slightly overestimated during the first half and underestimated during the second half of the melting process. This study is anticipated to provide a new solution for performance evaluation and fast prediction of LHSEs.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"237 ","pages":"Article 121896"},"PeriodicalIF":9.0,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663240","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.renene.2024.121907
Wenhu Sang , Yuxin Ma , Senyuan Li , Peng Xue , Bojia Li , Jinqing Peng , Man Fan
Except for irradiance and temperature, the distribution of solar spectrum also affects the electrical performance of photovoltaic (PV) modules. To explore the effect, this study conducted long-term experimental measurements on the wide range of full solar radiation spectrum with monocrystalline silicon (m-Si) and cadmium telluride (CdTe), and established new spectral correction function (SCF) under horizontal conditions based on the average photon energy (APE). It is verified with a good agreement R2 of 0.95, and the maximum RMSE is only 0.017 %. Moreover, the application of the SCFs to building vertical façade has also been well verified, and the accuracy of electrical performance prediction can be improved by 14.51 % (m-Si) and 3.57 % (CdTe). In addition, this study combines the annual horizontal total solar radiation spectrum in Beijing and gives the annual spectral gain and loss (SGL) ratio of two PV panels. The power generation performance of the two PV modules under the actual spectrum will be underestimated for about 53.5 % and 99.7 % of the time in the whole year. This study broadens the dimension of evaluating the electrical performance parameters of PV panels and provides a basis and guidance for the accurate prediction and calculation of photovoltaic power generation.
{"title":"Spectral correction of photovoltaic module electrical properties","authors":"Wenhu Sang , Yuxin Ma , Senyuan Li , Peng Xue , Bojia Li , Jinqing Peng , Man Fan","doi":"10.1016/j.renene.2024.121907","DOIUrl":"10.1016/j.renene.2024.121907","url":null,"abstract":"<div><div>Except for irradiance and temperature, the distribution of solar spectrum also affects the electrical performance of photovoltaic (PV) modules. To explore the effect, this study conducted long-term experimental measurements on the wide range of full solar radiation spectrum with monocrystalline silicon (m-Si) and cadmium telluride (CdTe), and established new spectral correction function (SCF) under horizontal conditions based on the average photon energy (APE). It is verified with a good agreement R<sup>2</sup> of 0.95, and the maximum RMSE is only 0.017 %. Moreover, the application of the SCFs to building vertical façade has also been well verified, and the accuracy of electrical performance prediction can be improved by 14.51 % (m-Si) and 3.57 % (CdTe). In addition, this study combines the annual horizontal total solar radiation spectrum in Beijing and gives the annual spectral gain and loss (SGL) ratio of two PV panels. The power generation performance of the two PV modules under the actual spectrum will be underestimated for about 53.5 % and 99.7 % of the time in the whole year. This study broadens the dimension of evaluating the electrical performance parameters of PV panels and provides a basis and guidance for the accurate prediction and calculation of photovoltaic power generation.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"237 ","pages":"Article 121907"},"PeriodicalIF":9.0,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663169","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 : 2024-11-12DOI: 10.1016/j.renene.2024.121869
Liu Yang , Yiyang Ye , Yuhao Qiao , Hengli Feng , Jingduo Wang , Mei Dou , Yanwen Wu , Qimeng Cao , Yan Liu
Utilizing solar energy is essential for achieving zero carbon emissions in buildings, especially in solar enrichment zone, such as the Qinghai-Tibet Plateau. Given a climate characterized by low temperatures and high solar radiation, the thermal performance of south-facing envelopes is crucial for the collection and utilization of solar radiation, necessitating detailed field investigations. This study conducted field measurements on three typical dwellings in Litang County and Ngari Prefecture during winter and transition seasons. Transparent envelopes had heat gains of 20.7 MJ⋅m-2 but lost 26.4 MJ⋅m-2, with indoor temperature fluctuation of 17.2 °C. Opaque envelopes gained 132.3 W⋅m-2 from solar radiation and lost 99.3 W⋅m-2 through convection, storing 2.5 MJ⋅m-2 with over 85 % lost to the outdoor environment. This research revealed the dynamic thermal characteristics of heat collection, storage, and insulation performance of south-facing envelopes in local climate, indicated that the existing thermal performance is not yet sufficient to cope with the low temperature and high radiation climate. This work can provide reference for the development of solar utilization envelopes for the Qinghai-Tibet Plateau.
{"title":"Thermal performance of south-facing envelopes in solar enrichment zone of Qinghai–Tibet plateau: Field measurements of multiple dwellings in winter and transition seasons","authors":"Liu Yang , Yiyang Ye , Yuhao Qiao , Hengli Feng , Jingduo Wang , Mei Dou , Yanwen Wu , Qimeng Cao , Yan Liu","doi":"10.1016/j.renene.2024.121869","DOIUrl":"10.1016/j.renene.2024.121869","url":null,"abstract":"<div><div>Utilizing solar energy is essential for achieving zero carbon emissions in buildings, especially in solar enrichment zone, such as the Qinghai-Tibet Plateau. Given a climate characterized by low temperatures and high solar radiation, the thermal performance of south-facing envelopes is crucial for the collection and utilization of solar radiation, necessitating detailed field investigations. This study conducted field measurements on three typical dwellings in Litang County and Ngari Prefecture during winter and transition seasons. Transparent envelopes had heat gains of 20.7 MJ⋅m<sup>-</sup><sup>2</sup> but lost 26.4 MJ⋅m<sup>-</sup><sup>2</sup>, with indoor temperature fluctuation of 17.2 °C. Opaque envelopes gained 132.3 W⋅m<sup>-</sup><sup>2</sup> from solar radiation and lost 99.3 W⋅m<sup>-</sup><sup>2</sup> through convection, storing 2.5 MJ⋅m<sup>-</sup><sup>2</sup> with over 85 % lost to the outdoor environment. This research revealed the dynamic thermal characteristics of heat collection, storage, and insulation performance of south-facing envelopes in local climate, indicated that the existing thermal performance is not yet sufficient to cope with the low temperature and high radiation climate. This work can provide reference for the development of solar utilization envelopes for the Qinghai-Tibet Plateau.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"237 ","pages":"Article 121869"},"PeriodicalIF":9.0,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662804","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 : 2024-11-12DOI: 10.1016/j.renene.2024.121880
Zhongrui Wang, Yulin Ma, Lei Chen, Tingyu Yan, Ningzhao Shang, Huiliang Li, Yunan Han, Xue Liu
Selective transformation of biomass platform molecule 5-hydroxymethylfurfural (HMF) into biofuel 2,5-dimethylfuran (DMF) through hydrogenolysis path has attracted significant attention in the field of biomass catalytic conversion. In general, this process required supported noble catalysts under elevated temperature (423–533 K) and enough H2 pressure (1–4 MPa). Herein, Co/Beta-DA catalysts with various Co loadings of 5–20 wt% were post-synthesized through wet impregnation. Benefiting from the relatively open channel systems and strong metal-support interactions, the obtained 10Co/Beta-DA with ∼10 wt% Co contents facilitated the hydrogenation of C=O bonds and the cleavage of C-O bonds, which was efficient for the selective hydrogenolysis from HMF to DMF (Conv.HMF ≥ 99.9 %, Sel.DMF ≥ 99.9 %) under mild reaction conditions (H2 pressure, 1.0 MPa; temp., 423 K; time, 3 h). The innovative strategy of designing and preparing rational non-noble impregnated zeolite provided brand-new perspectives to solve the problems of high cost and harsh reaction conditions in the HMF hydrogenolysis process, which has the potential to convert biomass into renewable liquid fuels.
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