Pub Date : 2025-02-04DOI: 10.1016/j.renene.2025.122603
Waheed Ullah Shah , Ijaz Younis , Ibtissem Missaoui , Xiyu Liu
This study explores volatility and the environmental transition effect of renewable energy and fintech markets on the European real estate market during the COVID-19 pandemic and the Russia-Ukraine conflict using the TVP-VAR connectedness approach. Overall, our study found strong connectedness between renewable energy, fintech, and European real estate stock markets in both crises from January 2019 to January 2024. During COVID-19, all renewable energy and European real estate markets are key net volatility spillover receivers; whereas fintech and other real estate markets are net spillover transmitters. In the Russia-Ukraine conflict, fintech, renewable energy, and the European real estate marketsare the prime net risk spillover receivers; whereas NEX and other real estate markets are net spillover transmitters. However, market returns further decreased in the Russia-Ukraine conflict compared to COVID-19. Our study provides significant and insightful policy implications for financial, digital, green, commercial, and residential real estate, and related industry stakeholders in Europe.
{"title":"Environmental transitions effect of renewable energy and fintech markets on Europe's real estate stock market","authors":"Waheed Ullah Shah , Ijaz Younis , Ibtissem Missaoui , Xiyu Liu","doi":"10.1016/j.renene.2025.122603","DOIUrl":"10.1016/j.renene.2025.122603","url":null,"abstract":"<div><div>This study explores volatility and the environmental transition effect of renewable energy and fintech markets on the European real estate market during the COVID-19 pandemic and the Russia-Ukraine conflict using the TVP-VAR connectedness approach. Overall, our study found strong connectedness between renewable energy, fintech, and European real estate stock markets in both crises from January 2019 to January 2024. During COVID-19, all renewable energy and European real estate markets are key net volatility spillover receivers; whereas fintech and other real estate markets are net spillover transmitters. In the Russia-Ukraine conflict, fintech, renewable energy, and the European real estate marketsare the prime net risk spillover receivers; whereas NEX and other real estate markets are net spillover transmitters. However, market returns further decreased in the Russia-Ukraine conflict compared to COVID-19. Our study provides significant and insightful policy implications for financial, digital, green, commercial, and residential real estate, and related industry stakeholders in Europe.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"243 ","pages":"Article 122603"},"PeriodicalIF":9.0,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143350666","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 : 2025-02-04DOI: 10.1016/j.renene.2025.122614
Tanimu Jatau, Tunde Bello-Ochende, Arnaud G. Malan
This study presents a novel design for a swirling jet impingement cooling heat sink with and without internal pin-fins, integrated into a high concentrator photovoltaic system. The investigation was carried out under a concentration ratio of 1000 suns and Reynolds numbers ranging from 1000 to 5000 with the inlet temperature of 25 °C. The performance of the heat sink was evaluated using different design target-to-jet diameter ratios of 2, 3, 4 and 5, with the aim of identifying the best design that provides effective cooling of the solar cell. The results obtained revealed that the average cell temperature decreases as the Reynolds number increases for both the heat sink with and without internal fins for all the target-to-jet diameter ratios. A comparison of the average cell temperature showed that the heat sink with internal fins achieved lower average cell temperatures than the heat sink without internal fins across all target-to-jet diameter ratios, except for a target-to-jet diameter ratio of 4 which recorded the lowest average cell temperature of 311.56 K, corresponding to the highest cell efficiency of 40.04 % at a Reynolds number of 5000. The numerical calculations were conducted using CFD code and verified with the available data in an open literature.
{"title":"Novel design of swirling jet impingement heat sink with and without internal Pin-Fins for thermal management of high-concentrator photovoltaic systems","authors":"Tanimu Jatau, Tunde Bello-Ochende, Arnaud G. Malan","doi":"10.1016/j.renene.2025.122614","DOIUrl":"10.1016/j.renene.2025.122614","url":null,"abstract":"<div><div>This study presents a novel design for a swirling jet impingement cooling heat sink with and without internal pin-fins, integrated into a high concentrator photovoltaic system. The investigation was carried out under a concentration ratio of 1000 suns and Reynolds numbers ranging from 1000 to 5000 with the inlet temperature of 25 °C. The performance of the heat sink was evaluated using different design target-to-jet diameter ratios of 2, 3, 4 and 5, with the aim of identifying the best design that provides effective cooling of the solar cell. The results obtained revealed that the average cell temperature decreases as the Reynolds number increases for both the heat sink with and without internal fins for all the target-to-jet diameter ratios. A comparison of the average cell temperature showed that the heat sink with internal fins achieved lower average cell temperatures than the heat sink without internal fins across all target-to-jet diameter ratios, except for a target-to-jet diameter ratio of 4 which recorded the lowest average cell temperature of 311.56 K, corresponding to the highest cell efficiency of 40.04 % at a Reynolds number of 5000. The numerical calculations were conducted using CFD code and verified with the available data in an open literature.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"243 ","pages":"Article 122614"},"PeriodicalIF":9.0,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143350657","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 : 2025-02-04DOI: 10.1016/j.renene.2025.122589
Asma Altaf , Muhammad Awais Anwar , U. Shahzad , Yuriy Bilan
UN Sustainable Development Goals 13 and 7 on climate change mitigation and clean and responsible energy use serve as the driving forces behind this study. In the light of growing global concern, the current study seeks to evaluate the effect of renewable energy, green finance and institutional quality on carbon dioxide (CO2) emissions in the “Organization for Economic Co-operation and Development” (OECD) members from 2000 to 2022. To ascertain the impact of the relationship between these variables, the study employed the panel quantile autoregressive distributed lag (PQARDL). The cointegration test supports the validity of the long-term link between variables of the study. Apart from that, the estimated results have supported an inverted U-shaped link between CO2 and renewable energy over the long term in the median base (0.50) quantile group. These findings support the global sustainability agenda by illuminating the potential impact of robust institutional frameworks, renewable energy and sustainable finance practices on CO2 reductions. It also recommends that in order to achieve environmental sustainability and enhance environmental quality by lowering CO2, OECD policy maker should prioritize the use of renewable energy sources and high-quality institutions.
{"title":"Exploring the nexus among green finance, renewable energy and environmental sustainability: Evidence from OECD economies","authors":"Asma Altaf , Muhammad Awais Anwar , U. Shahzad , Yuriy Bilan","doi":"10.1016/j.renene.2025.122589","DOIUrl":"10.1016/j.renene.2025.122589","url":null,"abstract":"<div><div>UN Sustainable Development Goals 13 and 7 on climate change mitigation and clean and responsible energy use serve as the driving forces behind this study. In the light of growing global concern, the current study seeks to evaluate the effect of renewable energy, green finance and institutional quality on carbon dioxide (CO<sub>2</sub>) emissions in the “Organization for Economic Co-operation and Development” (OECD) members from 2000 to 2022. To ascertain the impact of the relationship between these variables, the study employed the panel quantile autoregressive distributed lag (PQARDL). The cointegration test supports the validity of the long-term link between variables of the study. Apart from that, the estimated results have supported an inverted U-shaped link between CO<sub>2</sub> and renewable energy over the long term in the median base (0.50) quantile group. These findings support the global sustainability agenda by illuminating the potential impact of robust institutional frameworks, renewable energy and sustainable finance practices on CO<sub>2</sub> reductions. It also recommends that in order to achieve environmental sustainability and enhance environmental quality by lowering CO<sub>2</sub>, OECD policy maker should prioritize the use of renewable energy sources and high-quality institutions.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"244 ","pages":"Article 122589"},"PeriodicalIF":9.0,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143436875","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 : 2025-02-04DOI: 10.1016/j.renene.2025.122604
F. De Bettin , F.D. Minuto , D.S. Schiera , A. Lanzini
Properly sizing renewable energy sources is crucial for ensuring their techno-economic viability, especially under policies promoting solar power through photovoltaics (PV). The inherent variability of PV production requires assessing energy yield and self-consumption at different time scales, along with their associated uncertainties, to evaluate technical performance and financial risks. This challenge is critical for solar renewable energy communities (RECs), where energy sharing determines performance quality.
This work introduces a framework that quantifies the impact of PV's stochastic nature on energy sharing uncertainty in RECs. Tested across seven locations in Italy leveraging PVGIS data, the framework integrates path-integral and Fokker-Planck formalisms with a Monte Carlo approach, and is demonstrated to effectively capture production variability and energy yields.
For each location, 10,000 synthetic profiles were generated for a 50 kW peak power plant connected to the grid, serving 100 residential consumers with a typical consumption profile representative of the area. The relative uncertainty in yearly shared energy proved to range from 2 % to 3 %.
Comparisons with benchmark methods, like averaged hourly production (AHP) and typical meteorological year (TMY) profiles, revealed an systematic overestimation of shared energy during months of production surplus, underscoring the need of accounting for stochasticity in energy modeling.
{"title":"Evaluating uncertainty of shared energy in solar energy communities using a stochastic simulation framework","authors":"F. De Bettin , F.D. Minuto , D.S. Schiera , A. Lanzini","doi":"10.1016/j.renene.2025.122604","DOIUrl":"10.1016/j.renene.2025.122604","url":null,"abstract":"<div><div>Properly sizing renewable energy sources is crucial for ensuring their techno-economic viability, especially under policies promoting solar power through photovoltaics (PV). The inherent variability of PV production requires assessing energy yield and self-consumption at different time scales, along with their associated uncertainties, to evaluate technical performance and financial risks. This challenge is critical for solar renewable energy communities (RECs), where energy sharing determines performance quality.</div><div>This work introduces a framework that quantifies the impact of PV's stochastic nature on energy sharing uncertainty in RECs. Tested across seven locations in Italy leveraging PVGIS data, the framework integrates path-integral and Fokker-Planck formalisms with a Monte Carlo approach, and is demonstrated to effectively capture production variability and energy yields.</div><div>For each location, 10,000 synthetic profiles were generated for a 50 kW peak power plant connected to the grid, serving 100 residential consumers with a typical consumption profile representative of the area. The relative uncertainty in yearly shared energy proved to range from 2 % to 3 %.</div><div>Comparisons with benchmark methods, like averaged hourly production (AHP) and typical meteorological year (TMY) profiles, revealed an systematic overestimation of shared energy during months of production surplus, underscoring the need of accounting for stochasticity in energy modeling.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"243 ","pages":"Article 122604"},"PeriodicalIF":9.0,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143369791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-03DOI: 10.1016/j.renene.2025.122552
Xiaowei Gan , Zhengjie Chen , Wenhui Ma , Xiaowei Chen
Coffee wastewater (CWW), a byproduct of the coffee-making process, can serve as a grinding medium in the co-pyrolysis gasification of coal and biomass. In this study, the effects of three wet grinding media—water, alcohol, and CWW—were compared with dry grinding for the first time, and their influences on the response characteristics of mixed carbon materials were investigated. Changes in carbon structure, graphitization, morphology, surface area, and pore size were analyzed through XRD, Raman, SEM, and BET technique. Reaction activation energy was evaluated through the Friedman, Kissinger–Akahira–Sunose, Flynn–Wall–Ozawa, and Starink methods, while the reaction model was investigated through the integral master curve method. The results showed that CWW-based grinding exhibited the highest reactivity, with an average weightlessness end point of 98.03 %. CWW-based grinding exhibited a specific surface area of 2.0228 m2/g, d002 of 0.3777 nm, La of 2.204 nm, and Lc of 0.9119 nm, representing increases of 20.10, 2.18, 16.61 and 11.33 %, respectively, compared with dry grinding. In addition, the average pore diameter of CWW-based grinding was 23.90 % lower. Moreover, the average activation energy decreased from 147.03 kJ/mol for dry grinding to 110.14 kJ/mol for CWW-based grinding. The average activation energy and enthalpy change for the reactions based on the four grinding media followed this order: CWW-based grinding < alcohol-based grinding < H2O-based grinding < dry grinding. Wet grinding of mixed carbon materials using CWW can modify the carbon skeleton structure, refine particle size, and shift the reaction mechanism from exponential nucleation to diffusion. Overall, this study proposes a new method to enhance the pyrolysis gasification of carbon materials, and can provide new ideas for the industrial silicon industry.
{"title":"Effects of wet grinding of coffee wastewater on co-pyrolytic gasification of composite carbon materials: Reaction properties, thermodynamics and gasification kinetics, integral master diagram method and carbon material structure","authors":"Xiaowei Gan , Zhengjie Chen , Wenhui Ma , Xiaowei Chen","doi":"10.1016/j.renene.2025.122552","DOIUrl":"10.1016/j.renene.2025.122552","url":null,"abstract":"<div><div>Coffee wastewater (CWW), a byproduct of the coffee-making process, can serve as a grinding medium in the co-pyrolysis gasification of coal and biomass. In this study, the effects of three wet grinding media—water, alcohol, and CWW—were compared with dry grinding for the first time, and their influences on the response characteristics of mixed carbon materials were investigated. Changes in carbon structure, graphitization, morphology, surface area, and pore size were analyzed through XRD, Raman, SEM, and BET technique. Reaction activation energy was evaluated through the Friedman, Kissinger–Akahira–Sunose, Flynn–Wall–Ozawa, and Starink methods, while the reaction model was investigated through the integral master curve method. The results showed that CWW-based grinding exhibited the highest reactivity, with an average weightlessness end point of 98.03 %. CWW-based grinding exhibited a specific surface area of 2.0228 m<sup>2</sup>/g, d<sub>002</sub> of 0.3777 nm, La of 2.204 nm, and Lc of 0.9119 nm, representing increases of 20.10, 2.18, 16.61 and 11.33 %, respectively, compared with dry grinding. In addition, the average pore diameter of CWW-based grinding was 23.90 % lower. Moreover, the average activation energy decreased from 147.03 kJ/mol for dry grinding to 110.14 kJ/mol for CWW-based grinding. The average activation energy and enthalpy change for the reactions based on the four grinding media followed this order: CWW-based grinding < alcohol-based grinding < H<sub>2</sub>O-based grinding < dry grinding. Wet grinding of mixed carbon materials using CWW can modify the carbon skeleton structure, refine particle size, and shift the reaction mechanism from exponential nucleation to diffusion. Overall, this study proposes a new method to enhance the pyrolysis gasification of carbon materials, and can provide new ideas for the industrial silicon industry.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"243 ","pages":"Article 122552"},"PeriodicalIF":9.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143272612","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 : 2025-02-03DOI: 10.1016/j.renene.2025.122610
Qingwen Xue , Ao Wang , Sihang Jiang , Zhichao Wang , Yingxia Yang , Yuanda Cheng , Zhonghai Zheng
The energy systems configuration in nearly-zero energy buildings (NZEBs) has traditionally been optimized under deterministic conditions. However, building energy load often exhibiteds uncertainties in practice, influenced by factors such as occupant behavior and weather conditions. These uncertainties may lead to suboptimal solutions of capacity configuration or failure in achieving building design targets. This study introduces an stochastic optimization method for energy systems configuration that accounts for load uncertainties. The process begins with the characterization of uncertain parameters, followed by the construction of a scenario set, and concludes with the multi-objective optimization within a 70%–90% load guarantee. The NSGA-II, coupled with entropy weight-TOPSIS method, was utilized to formulate and solve the multi-objective optimization problem. This approach was then compared with results obtained under deterministic and robust conditions based on load guarantee rate, cost, and carbon emissions. The results show that the most optimal solution was obtained by the stochastic optimization with a load guarantee rate of 90%, which decreases equipment investment by 58.61% and carbon emissions by 15.8 %, and increases load guarantee rate by 133.69% compared to the initial design. These results underscore the significant effectiveness of incorporating load uncertainties in designing robust and flexible energy systems in NZEBs.
{"title":"Stochastic optimization of energy systems configuration for nearly-zero energy buildings considering load uncertainties","authors":"Qingwen Xue , Ao Wang , Sihang Jiang , Zhichao Wang , Yingxia Yang , Yuanda Cheng , Zhonghai Zheng","doi":"10.1016/j.renene.2025.122610","DOIUrl":"10.1016/j.renene.2025.122610","url":null,"abstract":"<div><div>The energy systems configuration in nearly-zero energy buildings (NZEBs) has traditionally been optimized under deterministic conditions. However, building energy load often exhibiteds uncertainties in practice, influenced by factors such as occupant behavior and weather conditions. These uncertainties may lead to suboptimal solutions of capacity configuration or failure in achieving building design targets. This study introduces an stochastic optimization method for energy systems configuration that accounts for load uncertainties. The process begins with the characterization of uncertain parameters, followed by the construction of a scenario set, and concludes with the multi-objective optimization within a 70%–90% load guarantee. The NSGA-II, coupled with entropy weight-TOPSIS method, was utilized to formulate and solve the multi-objective optimization problem. This approach was then compared with results obtained under deterministic and robust conditions based on load guarantee rate, cost, and carbon emissions. The results show that the most optimal solution was obtained by the stochastic optimization with a load guarantee rate of 90%, which decreases equipment investment by 58.61% and carbon emissions by 15.8 %, and increases load guarantee rate by 133.69% compared to the initial design. These results underscore the significant effectiveness of incorporating load uncertainties in designing robust and flexible energy systems in NZEBs.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"243 ","pages":"Article 122610"},"PeriodicalIF":9.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378559","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 : 2025-02-03DOI: 10.1016/j.renene.2025.122605
Zhitao Zhang , Arshad Ahmad Khan , Zonglin Wang
Science and technology finance integrates the efficiency-enhancing function of digital technology and the macro-regulatory function of finance, which plays a crucial role in promoting renewable energy development. This paper utilizes panel data from 2004 to 2020, covering 3l provinces in China as the research sample. We empirically analyze the impact effect of science and technology finance on renewable energy development. The results show that it is 0.0778 that the average impact effect for science and technology finance to improve the development of renewable energy. As the science and technology finance and renewable energy development improves, there is a tendency for the positive marginal effect to increase, specifically when the level of renewable energy development is 70 % the promotion effect reaches a maximum value of 0.1181. Science and technology finance can indirectly promote the development of renewable energy by improving the level of green technology innovation. In the process of science and technology finance affecting renewable energy development, there is an average negative moderating effect of 0.3686 on environmental regulation. In addition, this paper considers the regional heterogeneity and the promotion effect of science and technology finance renewable energy development in different energy transition stages.
{"title":"How does science and technology finance affect the renewable energy development? Evidence from China","authors":"Zhitao Zhang , Arshad Ahmad Khan , Zonglin Wang","doi":"10.1016/j.renene.2025.122605","DOIUrl":"10.1016/j.renene.2025.122605","url":null,"abstract":"<div><div>Science and technology finance integrates the efficiency-enhancing function of digital technology and the macro-regulatory function of finance, which plays a crucial role in promoting renewable energy development. This paper utilizes panel data from 2004 to 2020, covering 3l provinces in China as the research sample. We empirically analyze the impact effect of science and technology finance on renewable energy development. The results show that it is 0.0778 that the average impact effect for science and technology finance to improve the development of renewable energy. As the science and technology finance and renewable energy development improves, there is a tendency for the positive marginal effect to increase, specifically when the level of renewable energy development is 70 % the promotion effect reaches a maximum value of 0.1181. Science and technology finance can indirectly promote the development of renewable energy by improving the level of green technology innovation. In the process of science and technology finance affecting renewable energy development, there is an average negative moderating effect of 0.3686 on environmental regulation. In addition, this paper considers the regional heterogeneity and the promotion effect of science and technology finance renewable energy development in different energy transition stages.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"243 ","pages":"Article 122605"},"PeriodicalIF":9.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143272615","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 : 2025-02-03DOI: 10.1016/j.renene.2025.122582
Konstantinos Braimakis , Angeliki Kitsopoulou , Tryfon C. Roumpedakis , George M. Stavrakakis , Christos Tzivanidis
The thermodynamic and economic performance of a solar ejector cooling cycle (SECC) driven by flat plate collectors is investigated considering its part-load behavior. An SECC operating with R1234ze(E) is optimized for different collector areas from 50 to 150 m2 and specific storage tank volumes from 50 lt/m2 to 150 lt/m2 per collector area, considering climate data of four cities (Athens, Madrid, Nicosia and Rome) to maximize seasonal cooling according to partial and full day cooling schedules. The ECC optimization variables are the nominal heat transfer fluid (HTF) temperature at the generator inlet and cooling fluid temperature at the condenser/subcooler inlet. Optimal HTF and cooling fluid temperatures are 86–87 °C and 30–33 °C, respectively, showing minor variation for different conditions. The produced cooling is lowest in Nicosia (4–24 kWhc/m2) and highest in Madrid and Athens (17–45 kWhc and 12–38 kWhc/m2), with significantly improved performance under full day cooling schedule. Higher specific tank volumes result in slight and significant increase in cooling production under the partial and full day cooling schedules, respectively. According to techno-economic results, the investigated SECC is a non-viable solar thermal cooling option because of its poor solar cooling conversion efficiency.
{"title":"Part-load based optimization of solar ejector cooling cycle","authors":"Konstantinos Braimakis , Angeliki Kitsopoulou , Tryfon C. Roumpedakis , George M. Stavrakakis , Christos Tzivanidis","doi":"10.1016/j.renene.2025.122582","DOIUrl":"10.1016/j.renene.2025.122582","url":null,"abstract":"<div><div>The thermodynamic and economic performance of a solar ejector cooling cycle (SECC) driven by flat plate collectors is investigated considering its part-load behavior. An SECC operating with R1234ze(E) is optimized for different collector areas from 50 to 150 m<sup>2</sup> and specific storage tank volumes from 50 lt/m<sup>2</sup> to 150 lt/m<sup>2</sup> per collector area, considering climate data of four cities (Athens, Madrid, Nicosia and Rome) to maximize seasonal cooling according to partial and full day cooling schedules. The ECC optimization variables are the nominal heat transfer fluid (HTF) temperature at the generator inlet and cooling fluid temperature at the condenser/subcooler inlet. Optimal HTF and cooling fluid temperatures are 86–87 °C and 30–33 °C, respectively, showing minor variation for different conditions. The produced cooling is lowest in Nicosia (4–24 kWh<sub>c</sub>/m<sup>2</sup>) and highest in Madrid and Athens (17–45 kWh<sub>c</sub> and 12–38 kWh<sub>c</sub>/m<sup>2</sup>), with significantly improved performance under full day cooling schedule. Higher specific tank volumes result in slight and significant increase in cooling production under the partial and full day cooling schedules, respectively. According to techno-economic results, the investigated SECC is a non-viable solar thermal cooling option because of its poor solar cooling conversion efficiency.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"243 ","pages":"Article 122582"},"PeriodicalIF":9.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143369795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-03DOI: 10.1016/j.renene.2025.122520
Md Abul Hasnat, Somayeh Asadi, Negin Alemazkoor
Accurate forecasting of solar power output from multiple photovoltaic plants simultaneously for different time horizons is crucial for their large-scale integration into the electric grid. Forecasting strategies for PV output power significantly vary depending on the forecasting horizons, quality, variety, resolution of data, and fields of application. While the researchers addressed many of these particular cases to achieve high forecasting accuracy, the literature lacks sufficient discussion on integrating strategies for various forecasting scenarios into a general framework. This article proposes such a framework facilitating PV power forecasting with variable time horizons and imparting data of various types and granularity by introducing a single adjustable module into the framework. Moreover, by proposing a geographic distance-based graph construction, ensuring minimal vertex connectivity and adjustable sparsity, the technique captures the spatiotemporal correlation among the PV plant through a graph attention network for accurate forecasting. The proposed technique is highly scalable archiving excellent forecasting performance (an average absolute error of of the plant capacity for 5 min to 3-day forecasting horizon) up to thousands of PVs. The results indicate that the proposed method outperforms state-of-the-art approaches, such as Long Short-Term Memory networks, in terms of both accuracy and scalability. Additionally, this paper analyzes the suitability of features for forecasting in different scenarios and performance sensitivity to various model parameters.
{"title":"A graph attention network framework for generalized-horizon multi-plant solar power generation forecasting using heterogeneous data","authors":"Md Abul Hasnat, Somayeh Asadi, Negin Alemazkoor","doi":"10.1016/j.renene.2025.122520","DOIUrl":"10.1016/j.renene.2025.122520","url":null,"abstract":"<div><div>Accurate forecasting of solar power output from multiple photovoltaic plants simultaneously for different time horizons is crucial for their large-scale integration into the electric grid. Forecasting strategies for PV output power significantly vary depending on the forecasting horizons, quality, variety, resolution of data, and fields of application. While the researchers addressed many of these particular cases to achieve high forecasting accuracy, the literature lacks sufficient discussion on integrating strategies for various forecasting scenarios into a general framework. This article proposes such a framework facilitating PV power forecasting with variable time horizons and imparting data of various types and granularity by introducing a single adjustable module into the framework. Moreover, by proposing a geographic distance-based graph construction, ensuring minimal vertex connectivity and adjustable sparsity, the technique captures the spatiotemporal correlation among the PV plant through a graph attention network for accurate forecasting. The proposed technique is highly scalable archiving excellent forecasting performance (an average absolute error of <span><math><mrow><mo><</mo><mn>5</mn><mtext>%</mtext></mrow></math></span> of the plant capacity for 5 min to 3-day forecasting horizon) up to thousands of PVs. The results indicate that the proposed method outperforms state-of-the-art approaches, such as Long Short-Term Memory networks, in terms of both accuracy and scalability. Additionally, this paper analyzes the suitability of features for forecasting in different scenarios and performance sensitivity to various model parameters.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"243 ","pages":"Article 122520"},"PeriodicalIF":9.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143369796","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 : 2025-02-03DOI: 10.1016/j.renene.2025.122601
Marcos Tostado-Véliz , Hany M. Hasanien , Manuel Gómez-González , Francisco Jurado
With the increase in the number of rooftop photovoltaic installations worldwide, end users are evolving from pure passive consumers, to more active agents capable to provide generation capacity (prosumers). This aspect becomes especially relevant in energy communities, conceived as groups of prosumers that actuate in a coordinated manner on pursuing common objectives. In this context, properly planning photovoltaic assets becomes crucial for ensuring the efficiency and economic profitability of energy communities. This paper addresses this issue. In particular, a novel rooftop planning tool for energy communities is developed. Unlike to other similar approaches, the new proposal devotes on individual rooftop assets, resulting more suitable to promote incentives to guide individual investments in own assets. The proposed methodology renders as a robust programming approach including a suitable representation of uncertainties via polyhedral envelopes. The resulting model is solved employing the well-known column-and-constraint generation algorithm and profusely tested on a benchmark energy community involved 8 prosumers connected to a 15-bus network. Results serve to validate the new proposal and demonstrate its effectiveness and efficiency. Moreover, the case study reveals the importance of installing photovoltaic systems, which are capable of reducing the total project cost by 19 %. On the other hand, the role of storage assets is also assessed, showing up that energy arbitrage provided by batteries reduces the project cost by 8 % even in the absence or with limited onsite generation.
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