Robert Becker Pickson; Peng Gui; Ai Chen;Robert Becker Pickson
Harvested from ORCID Public Data FileRobert Becker Pickson in OpenAIRE Elliot Boateng; Elliot Boateng
Harvested from ORCID Public Data FileElliot Boateng in OpenAIREThe Nigerian government is committed to sustaining rice production to meet national demand. Nevertheless, political tension and climate-induced stressors remain crucial constraints in achieving policy targets. This study examines whether climate change and political instability significantly threaten rice production in Nigeria. First, we employed nonparametric methods to estimate the country's rainfall and temperature trends between 1980Q1 and 2015Q4. Second, we employed the autoregressive distributed lag (ARDL) technique to examine the effects of climate change and political instability on rice production. The results show that while temperature has an increasing pattern, rainfall exhibits no significant trend. The findings from the ARDL estimate reveal that rice production responds negatively to temperature changes but is less sensitive to changes in rainfall. In addition, political instability adversely affects rice production in Nigeria. We argue that Nigeria's slow growth in rice production can be traced back to the impact of climate change and political tension in rice farming areas. As a result, reducing the overall degree of conflict to ensure political stability is critical to boosting the country's self-sufficiency in rice production. We also recommend that farmers be supported and trained to adopt improved rice varieties less prone to extreme climate events while supporting them with irrigation facilities to facilitate rice production.
6 citations 6 popularity Top 10% influence Average impulse Top 10% 
Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1007/s11356-023-26859-9&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu- Robert Becker Pickson; Peng Gui; Ai Chen;
Robert Becker Pickson
Harvested from ORCID Public Data FileRobert Becker Pickson in OpenAIRE Elliot Boateng; Elliot Boateng
Harvested from ORCID Public Data FileElliot Boateng in OpenAIREThe Nigerian government is committed to sustaining rice production to meet national demand. Nevertheless, political tension and climate-induced stressors remain crucial constraints in achieving policy targets. This study examines whether climate change and political instability significantly threaten rice production in Nigeria. First, we employed nonparametric methods to estimate the country's rainfall and temperature trends between 1980Q1 and 2015Q4. Second, we employed the autoregressive distributed lag (ARDL) technique to examine the effects of climate change and political instability on rice production. The results show that while temperature has an increasing pattern, rainfall exhibits no significant trend. The findings from the ARDL estimate reveal that rice production responds negatively to temperature changes but is less sensitive to changes in rainfall. In addition, political instability adversely affects rice production in Nigeria. We argue that Nigeria's slow growth in rice production can be traced back to the impact of climate change and political tension in rice farming areas. As a result, reducing the overall degree of conflict to ensure political stability is critical to boosting the country's self-sufficiency in rice production. We also recommend that farmers be supported and trained to adopt improved rice varieties less prone to extreme climate events while supporting them with irrigation facilities to facilitate rice production.
6 citations 6 popularity Top 10% influence Average impulse Top 10% Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1007/s11356-023-26859-9&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - ARC| Discovery Projects - Grant ID: DP220103881 Yu He;
Fengji Luo; Fengji Luo
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesFengji Luo in OpenAIRE Gianluca Ranzi; Gianluca Ranzi
Harvested from ORCID Public Data FileGianluca Ranzi in OpenAIRE1 citations 1 popularity Average influence Average impulse Average Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/tpwrs.2023.3309531&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - ARC| Discovery Projects - Grant ID: DP220103881 Yu He; Fengji Luo;
Fengji Luo
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesFengji Luo in OpenAIRE Gianluca Ranzi; Gianluca Ranzi
Harvested from ORCID Public Data FileGianluca Ranzi in OpenAIRE1 citations 1 popularity Average influence Average impulse Average Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/tpwrs.2023.3309531&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - ARC| Discovery Early Career Researcher Award - Grant ID: DE180100082 A.L. Herring;
A.L. Herring
Harvested from ORCID Public Data FileA.L. Herring in OpenAIRE C. Sun; R.T. Armstrong; R.T. Armstrong
Harvested from ORCID Public Data FileR.T. Armstrong in OpenAIRE M. Saadatfar; M. Saadatfar
Harvested from ORCID Public Data FileM. Saadatfar in OpenAIRE7 citations 7 popularity Average influence Average impulse Top 10% Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.ijggc.2022.103803&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - ARC| Discovery Early Career Researcher Award - Grant ID: DE180100082 A.L. Herring;
A.L. Herring
Harvested from ORCID Public Data FileA.L. Herring in OpenAIRE C. Sun; R.T. Armstrong; R.T. Armstrong
Harvested from ORCID Public Data FileR.T. Armstrong in OpenAIRE M. Saadatfar; M. Saadatfar
Harvested from ORCID Public Data FileM. Saadatfar in OpenAIRE7 citations 7 popularity Average influence Average impulse Top 10% Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.ijggc.2022.103803&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - EC| 2D-4-CO2 ,EC| ESTEEM3Huali Wu; Lingqi Huang; Janis Timoshenko; Kun Qi; Wensen Wang; Jiefeng Liu; Yang Zhang; Shaokang Yang; Eddy Petit; Valérie Flaud; Ji Li; Chrystelle Salameh; Philippe Miele;
Philippe Miele
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesPhilippe Miele in OpenAIRE Luc Lajaunie; Luc Lajaunie
Harvested from ORCID Public Data FileLuc Lajaunie in OpenAIRE Beatriz Roldán Cuenya; Beatriz Roldán Cuenya
Harvested from ORCID Public Data FileBeatriz Roldán Cuenya in OpenAIRE Dewei Rao; Dewei Rao
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesDewei Rao in OpenAIRE Damien Voiry; Damien Voiry
Harvested from ORCID Public Data FileDamien Voiry in OpenAIRE43 citations 43 popularity Average influence Top 10% impulse Top 1% Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1038/s41560-024-01461-6&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - EC| 2D-4-CO2 ,EC| ESTEEM3Huali Wu; Lingqi Huang; Janis Timoshenko; Kun Qi; Wensen Wang; Jiefeng Liu; Yang Zhang; Shaokang Yang; Eddy Petit; Valérie Flaud; Ji Li; Chrystelle Salameh; Philippe Miele;
Philippe Miele
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesPhilippe Miele in OpenAIRE Luc Lajaunie; Luc Lajaunie
Harvested from ORCID Public Data FileLuc Lajaunie in OpenAIRE Beatriz Roldán Cuenya; Beatriz Roldán Cuenya
Harvested from ORCID Public Data FileBeatriz Roldán Cuenya in OpenAIRE Dewei Rao; Dewei Rao
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesDewei Rao in OpenAIRE Damien Voiry; Damien Voiry
Harvested from ORCID Public Data FileDamien Voiry in OpenAIRE43 citations 43 popularity Average influence Top 10% impulse Top 1% Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1038/s41560-024-01461-6&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - Nasri, Shohreh; Nowdeh, Saber Arabi; Davoudkhani, Iraj Faraji; Moghaddam, Mohammad Jafar Hadidian; +3 Authors
Nasri, Shohreh
Harvested from ORCID Public Data FileNasri, Shohreh in OpenAIRE Nasri, Shohreh; Nowdeh, Saber Arabi; Davoudkhani, Iraj Faraji; Moghaddam, Mohammad Jafar Hadidian; Kalam, Akhtar;Nasri, Shohreh
Harvested from ORCID Public Data FileNasri, Shohreh in OpenAIRE Shahrokhi, Saman; Zand, Mohammad;Shahrokhi, Saman
Harvested from ORCID Public Data FileShahrokhi, Saman in OpenAIREIn this chapter, turbulent flow of water-based optimization (TFWO) inspired based on whirlpools created in turbulent flow of water is used to solve the maximum power point tracking (MPPT) of photovoltaic systems in partial shading conditions. The TFWO is used to determine the optimal duty cycle of the DC/DC converter with the objective of maximizing the extracted power of the photovoltaic system. The capability of proposed method is evaluated in different patterns of partial shading to achieve global optimal power. The simulation results showed that TFWO is able to track the global maximum power point (GMPP), successfully in PSC and also fast climate changing. The TFWO has a better tracking capability with faster tracking speed and accuracy than particle swarm optimization (PSO) in obtaining the GMPP. Moreover, the results indicate that the use of buck–boost converter led to faster and more accurate access to the global optimal point than the system equipped with boost converter. The results showed that photovoltaic system with boost converter cannot obtain global maximum power in climate changing condition and limited the efficiency of the MPPT algorithm, while the photovoltaic system with buck–boost converter could be tracked GMPP due to its wider operating area.
14 citations 14 popularity Top 10% influence Average impulse Top 10% Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1007/978-981-33-6456-1_12&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - Nasri, Shohreh; Nowdeh, Saber Arabi; Davoudkhani, Iraj Faraji; Moghaddam, Mohammad Jafar Hadidian; +3 Authors
Nasri, Shohreh
Harvested from ORCID Public Data FileNasri, Shohreh in OpenAIRE Nasri, Shohreh; Nowdeh, Saber Arabi; Davoudkhani, Iraj Faraji; Moghaddam, Mohammad Jafar Hadidian; Kalam, Akhtar;Nasri, Shohreh
Harvested from ORCID Public Data FileNasri, Shohreh in OpenAIRE Shahrokhi, Saman; Zand, Mohammad;Shahrokhi, Saman
Harvested from ORCID Public Data FileShahrokhi, Saman in OpenAIREIn this chapter, turbulent flow of water-based optimization (TFWO) inspired based on whirlpools created in turbulent flow of water is used to solve the maximum power point tracking (MPPT) of photovoltaic systems in partial shading conditions. The TFWO is used to determine the optimal duty cycle of the DC/DC converter with the objective of maximizing the extracted power of the photovoltaic system. The capability of proposed method is evaluated in different patterns of partial shading to achieve global optimal power. The simulation results showed that TFWO is able to track the global maximum power point (GMPP), successfully in PSC and also fast climate changing. The TFWO has a better tracking capability with faster tracking speed and accuracy than particle swarm optimization (PSO) in obtaining the GMPP. Moreover, the results indicate that the use of buck–boost converter led to faster and more accurate access to the global optimal point than the system equipped with boost converter. The results showed that photovoltaic system with boost converter cannot obtain global maximum power in climate changing condition and limited the efficiency of the MPPT algorithm, while the photovoltaic system with buck–boost converter could be tracked GMPP due to its wider operating area.
14 citations 14 popularity Top 10% influence Average impulse Top 10% Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1007/978-981-33-6456-1_12&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - Jun Di;
Jun Di
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesJun Di in OpenAIRE John E. Fletcher; John E. Fletcher
Harvested from ORCID Public Data FileJohn E. Fletcher in OpenAIRE Weili Li; Weili Li
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesWeili Li in OpenAIRE1 citations 1 popularity Top 10% influence Average impulse Average Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/tia.2022.3183558&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - Jun Di;
Jun Di
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesJun Di in OpenAIRE John E. Fletcher; John E. Fletcher
Harvested from ORCID Public Data FileJohn E. Fletcher in OpenAIRE Weili Li; Weili Li
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesWeili Li in OpenAIRE1 citations 1 popularity Top 10% influence Average impulse Average Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/tia.2022.3183558&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - Muhammad Zubair;
Muhammad Zubair
Harvested from ORCID Public Data FileMuhammad Zubair in OpenAIRE Omarelfarouq Elgack; Zafar Said;Omarelfarouq Elgack
Harvested from ORCID Public Data FileOmarelfarouq Elgack in OpenAIRENanotechnology, and particularly nanoparticles (NPs), command significant attention across diverse fields such as medicine, chemistry, and engineering. NPs are integrated into a base material through various techniques, such as dispersion, coating, and encapsulation. This review intends to offer an evaluation of the implementation of NPs in nuclear energy and radiation protection. Particular focus is given to nanofluids - suspensions of NPs in a base fluid - and their impact on the thermal–hydraulic and neutronic attributes of nuclear reactor cores. Discussion centers on how substituting water with nanofluids as a coolant can modify these characteristics. Computational modeling and experimental methods of nanofluids are examined, highlighting an enhanced heat transfer capacity under both regular and emergency reactor conditions. The influence of nanofluids on neutronic parameters, aiming for optimized operation and safety of nuclear reactors, is also explored. Furthermore, the potential of NPs for radiation shielding is considered, underlining their capability to attenuate radiation effects. Examples are drawn from the addition of NPs to containment structures in nuclear facilities and their use in radiotherapy, as well as enhancing radiation shielding in imaging techniques. Finally, the review encompasses the effects of nanofluids on surface oxidation and corrosion. The importance of these factors lies in their substantial impact on heat transport properties and potential for corrosive damage.
5 citations 5 popularity Top 10% influence Average impulse Top 10% Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.nucengdes.2023.112618&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - Muhammad Zubair;
Muhammad Zubair
Harvested from ORCID Public Data FileMuhammad Zubair in OpenAIRE Omarelfarouq Elgack; Zafar Said;Omarelfarouq Elgack
Harvested from ORCID Public Data FileOmarelfarouq Elgack in OpenAIRENanotechnology, and particularly nanoparticles (NPs), command significant attention across diverse fields such as medicine, chemistry, and engineering. NPs are integrated into a base material through various techniques, such as dispersion, coating, and encapsulation. This review intends to offer an evaluation of the implementation of NPs in nuclear energy and radiation protection. Particular focus is given to nanofluids - suspensions of NPs in a base fluid - and their impact on the thermal–hydraulic and neutronic attributes of nuclear reactor cores. Discussion centers on how substituting water with nanofluids as a coolant can modify these characteristics. Computational modeling and experimental methods of nanofluids are examined, highlighting an enhanced heat transfer capacity under both regular and emergency reactor conditions. The influence of nanofluids on neutronic parameters, aiming for optimized operation and safety of nuclear reactors, is also explored. Furthermore, the potential of NPs for radiation shielding is considered, underlining their capability to attenuate radiation effects. Examples are drawn from the addition of NPs to containment structures in nuclear facilities and their use in radiotherapy, as well as enhancing radiation shielding in imaging techniques. Finally, the review encompasses the effects of nanofluids on surface oxidation and corrosion. The importance of these factors lies in their substantial impact on heat transport properties and potential for corrosive damage.
5 citations 5 popularity Top 10% influence Average impulse Top 10% Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.nucengdes.2023.112618&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - Huan Zhao; Junhua Zhao;
Junhua Zhao
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesJunhua Zhao in OpenAIRE Jing Qiu; Gaoqi Liang; +3 AuthorsJing Qiu
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesJing Qiu in OpenAIRE Huan Zhao; Junhua Zhao; Junhua Zhao
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesJunhua Zhao in OpenAIRE Jing Qiu; Gaoqi Liang;Jing Qiu
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesJing Qiu in OpenAIRE Fushuan Wen; Fushuan Wen
Harvested from ORCID Public Data FileFushuan Wen in OpenAIRE Yusheng Xue; Yusheng Xue
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesYusheng Xue in OpenAIRE Zhao Yang Dong; Zhao Yang Dong
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesZhao Yang Dong in OpenAIRERisk preference is an important factor in electricity market strategy analysis and decision-making. The existing methods of risk preference analysis need to design and execute questionnaires or experiments on the subjects, and hence are costly and time-consuming for bidding in electricity markets. This article proposes a new method of data-driven risk preference analysis for power generation plants based on historical data and inverse reinforcement learning. Historical data are transformed to the transition function model according to the specific market mechanism. An adjusted inverse reinforcement learning model is thereafter proposed along with the optimization objective and technical constraints. The proposed method is tested in a simulated electricity market environment using the Australian Energy Market Operator (AEMO) day-ahead bidding data. Simulation results show that 1) thermal power plants prefer to adjust risk preferences within the day; 2) apart from the thermal power plants, the rest types of power plants are risk-neutral; 3) the daily risk preference trend of the thermal power plants varies in different seasons and is closely related to the load level.
17 citations 17 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/tsg.2020.3036525&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - Huan Zhao; Junhua Zhao;
Junhua Zhao
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesJunhua Zhao in OpenAIRE Jing Qiu; Gaoqi Liang; +3 AuthorsJing Qiu
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesJing Qiu in OpenAIRE Huan Zhao; Junhua Zhao; Junhua Zhao
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesJunhua Zhao in OpenAIRE Jing Qiu; Gaoqi Liang;Jing Qiu
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesJing Qiu in OpenAIRE Fushuan Wen; Fushuan Wen
Harvested from ORCID Public Data FileFushuan Wen in OpenAIRE Yusheng Xue; Yusheng Xue
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesYusheng Xue in OpenAIRE Zhao Yang Dong; Zhao Yang Dong
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesZhao Yang Dong in OpenAIRERisk preference is an important factor in electricity market strategy analysis and decision-making. The existing methods of risk preference analysis need to design and execute questionnaires or experiments on the subjects, and hence are costly and time-consuming for bidding in electricity markets. This article proposes a new method of data-driven risk preference analysis for power generation plants based on historical data and inverse reinforcement learning. Historical data are transformed to the transition function model according to the specific market mechanism. An adjusted inverse reinforcement learning model is thereafter proposed along with the optimization objective and technical constraints. The proposed method is tested in a simulated electricity market environment using the Australian Energy Market Operator (AEMO) day-ahead bidding data. Simulation results show that 1) thermal power plants prefer to adjust risk preferences within the day; 2) apart from the thermal power plants, the rest types of power plants are risk-neutral; 3) the daily risk preference trend of the thermal power plants varies in different seasons and is closely related to the load level.
17 citations 17 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/tsg.2020.3036525&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - Michael S.A. Kamel;
Michael S.A. Kamel
Harvested from ORCID Public Data FileMichael S.A. Kamel in OpenAIRE Ahmed Al-jumaili; Ahmed Al-jumaili
Harvested from ORCID Public Data FileAhmed Al-jumaili in OpenAIRE Michael Oelgemöller; Mohan V. Jacob;Michael Oelgemöller
Harvested from ORCID Public Data FileMichael Oelgemöller in OpenAIREOrganic photovoltaics (OPVs) have received considerable attention over the past two decades as a promising alternative to their inorganic counterparts. Although the power conversion efficiency (PCE) of OPVs has rapidly increased in the last ten years exceeding 18%, higher PCEs are still needed to commercialize this emerging technology. The weak light absorption, particularly at wavelengths outside the visible region, and the recombination losses of the photo-generated charge carriers represent the major challenges for the PCE of OPVS. The light harvest and survival of the photo-generated charge carriers within OPVs are restricted to multiple factors such as material properties and device engineering. The application of different types of inorganic nanoparticles (INPs) in OPVs has been reported by many researchers as an effective strategy to overcome most of the PCE limitations. Here, a comprehensive overview of the progress in the performance of OPVs due to the application of different INPs over the past decade is provided. This review also presents an in-depth analysis of the efficiency loss pathways at the different steps of the photovoltaic effect and how INPs can address these issues resulting in PCE enhancement of OPVs. Finally, the impacts of this approach on the stability and cost of the device in addition to challenges and outlook are discussed.
18 citations 18 popularity Top 10% influence Average impulse Top 10% Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.rser.2022.112661&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - Michael S.A. Kamel;
Michael S.A. Kamel
Harvested from ORCID Public Data FileMichael S.A. Kamel in OpenAIRE Ahmed Al-jumaili; Ahmed Al-jumaili
Harvested from ORCID Public Data FileAhmed Al-jumaili in OpenAIRE Michael Oelgemöller; Mohan V. Jacob;Michael Oelgemöller
Harvested from ORCID Public Data FileMichael Oelgemöller in OpenAIREOrganic photovoltaics (OPVs) have received considerable attention over the past two decades as a promising alternative to their inorganic counterparts. Although the power conversion efficiency (PCE) of OPVs has rapidly increased in the last ten years exceeding 18%, higher PCEs are still needed to commercialize this emerging technology. The weak light absorption, particularly at wavelengths outside the visible region, and the recombination losses of the photo-generated charge carriers represent the major challenges for the PCE of OPVS. The light harvest and survival of the photo-generated charge carriers within OPVs are restricted to multiple factors such as material properties and device engineering. The application of different types of inorganic nanoparticles (INPs) in OPVs has been reported by many researchers as an effective strategy to overcome most of the PCE limitations. Here, a comprehensive overview of the progress in the performance of OPVs due to the application of different INPs over the past decade is provided. This review also presents an in-depth analysis of the efficiency loss pathways at the different steps of the photovoltaic effect and how INPs can address these issues resulting in PCE enhancement of OPVs. Finally, the impacts of this approach on the stability and cost of the device in addition to challenges and outlook are discussed.
18 citations 18 popularity Top 10% influence Average impulse Top 10% Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.rser.2022.112661&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - Masoud Babaei; Mohammad Mahdi Soleymani;
Masoud Babaei
Harvested from ORCID Public Data FileMasoud Babaei in OpenAIRE S. M. Muyeen; Mohammad Taghi Hamidi Beheshti; +2 AuthorsS. M. Muyeen
Harvested from ORCID Public Data FileS. M. Muyeen in OpenAIRE Masoud Babaei; Mohammad Mahdi Soleymani;Masoud Babaei
Harvested from ORCID Public Data FileMasoud Babaei in OpenAIRE S. M. Muyeen; Mohammad Taghi Hamidi Beheshti;S. M. Muyeen
Harvested from ORCID Public Data FileS. M. Muyeen in OpenAIRE Mohsen Ghafouri; Mohsen Ghafouri
Harvested from ORCID Public Data FileMohsen Ghafouri in OpenAIRE Ahmadreza Abazari; Ahmadreza Abazari
Harvested from ORCID Public Data FileAhmadreza Abazari in OpenAIREAbstract In recent years, modern appliances with high electricity demand have played a significant role in residential energy consumption. Despite the positive impact of these appliances on the quality of life, they suffer from major drawbacks, such as serious environmental concerns and high electricity bills. This paper introduces a consolidated framework of load management to alleviate those drawbacks. Initially, benefiting from a demonstrative analysis of home energy consumption data, controllable and responsive appliances in smart home are identified. Then, the energy consumption pattern is reduced and shifted using flexible load models and better utilization of existing energy storage systems. This can be achieved through data mining approaches, i.e., density-based spatial clustering of application with noise (DBSCAN) method. In this technique, no sensor for detection or measurement instruments will be required, whose deployment incur cost to the system or increase security risk for consumers. In the following, one scheduling of using controllable appliances, which is formulated by convex optimization, is considered for the demand response (DR) program, provided that this plan doesn't affect customers’ priority and convenience. In the last stage, the deployment of energy storage systems, such as plug-in hybrid electric vehicles (PHEVs) and battery energy storage systems (BESS), is introduced to lower the energy cost and improve the performance of the proposed DR model. Simulation results of this demand response are compared with conventional k-clustering methods to confirm the economic superiority of the DBSCAN clustering technique using the data of a residential unit during three different scenarios.
32 citations 32 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.est.2021.102407&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - Masoud Babaei; Mohammad Mahdi Soleymani;
Masoud Babaei
Harvested from ORCID Public Data FileMasoud Babaei in OpenAIRE S. M. Muyeen; Mohammad Taghi Hamidi Beheshti; +2 AuthorsS. M. Muyeen
Harvested from ORCID Public Data FileS. M. Muyeen in OpenAIRE Masoud Babaei; Mohammad Mahdi Soleymani;Masoud Babaei
Harvested from ORCID Public Data FileMasoud Babaei in OpenAIRE S. M. Muyeen; Mohammad Taghi Hamidi Beheshti;S. M. Muyeen
Harvested from ORCID Public Data FileS. M. Muyeen in OpenAIRE Mohsen Ghafouri; Mohsen Ghafouri
Harvested from ORCID Public Data FileMohsen Ghafouri in OpenAIRE Ahmadreza Abazari; Ahmadreza Abazari
Harvested from ORCID Public Data FileAhmadreza Abazari in OpenAIREAbstract In recent years, modern appliances with high electricity demand have played a significant role in residential energy consumption. Despite the positive impact of these appliances on the quality of life, they suffer from major drawbacks, such as serious environmental concerns and high electricity bills. This paper introduces a consolidated framework of load management to alleviate those drawbacks. Initially, benefiting from a demonstrative analysis of home energy consumption data, controllable and responsive appliances in smart home are identified. Then, the energy consumption pattern is reduced and shifted using flexible load models and better utilization of existing energy storage systems. This can be achieved through data mining approaches, i.e., density-based spatial clustering of application with noise (DBSCAN) method. In this technique, no sensor for detection or measurement instruments will be required, whose deployment incur cost to the system or increase security risk for consumers. In the following, one scheduling of using controllable appliances, which is formulated by convex optimization, is considered for the demand response (DR) program, provided that this plan doesn't affect customers’ priority and convenience. In the last stage, the deployment of energy storage systems, such as plug-in hybrid electric vehicles (PHEVs) and battery energy storage systems (BESS), is introduced to lower the energy cost and improve the performance of the proposed DR model. Simulation results of this demand response are compared with conventional k-clustering methods to confirm the economic superiority of the DBSCAN clustering technique using the data of a residential unit during three different scenarios.
32 citations 32 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.est.2021.102407&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu
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