A secured and scalable Peer-to-Peer (P2P) energy trading platform can facilitate the integration of renewable energy and thus contribute to building sustainable energy infrastructure. The decentralized architecture of...A secured and scalable Peer-to-Peer (P2P) energy trading platform can facilitate the integration of renewable energy and thus contribute to building sustainable energy infrastructure. The decentralized architecture of blockchain makes it a befitting candidate to actualize an efficient P2P energy trading market. However, for a sustainable and dynamic blockchain-based P2P energy trading platform, few critical aspects such as security, privacy and scalability need to be addressed with high priority. This paper proposes a blockchain-based solution for energy trading among the consumers which ensures the systems’ security, protects users’ privacy, and improves the overall scalability. More specifically, we develop a multilayered semi-permissioned blockchain-based platform to facilitate energy transactions. The practical byzantine fault tolerant algorithm is employed as the underlying consensus for verification and validation of transactions which ensures the system’s tolerance against internal error and malicious attacks. Additionally, we introduce the idea of quality of transaction (QoT)—a reward system for the participants of the network that eventually helps determine the participant’s eligibility for future transactions. The resiliency of the framework against the transaction malleability attack is demonstrated with two uses cases. Finally, a qualitative analysis is presented to indicate the system’s usefulness in improving the overall security, privacy, and scalability of the network.展开更多
A novel microgrid control strategy is presented in this paper. A resilient community microgrid model, which is equipped with solar PV generation and electric vehicles (EVs) and an improved inverter control system, is ...A novel microgrid control strategy is presented in this paper. A resilient community microgrid model, which is equipped with solar PV generation and electric vehicles (EVs) and an improved inverter control system, is considered. To fully exploit the capability of the community microgrid to operate in either grid-connected mode or islanded mode, as well as to achieve improved stability of the microgrid system, universal droop control, virtual inertia control, and a reinforcement learning-based control mechanism are combined in a cohesive manner, in which adaptive control parameters are determined online to tune the influence of the controllers. The microgrid model and control mechanisms are implemented in MATLAB/Simulink and set up in real-time simulation to test the feasibility and effectiveness of the proposed model. Experiment results reveal the effectiveness of regulating the controller’s frequency and voltage for various operating conditions and scenarios of a microgrid.展开更多
In the present paper, different Autoregressive Integrated Moving Average (ARIMA) models were developed to model the carbon dioxide emission by using time series data of forty-four years from 1972-2015. The performance...In the present paper, different Autoregressive Integrated Moving Average (ARIMA) models were developed to model the carbon dioxide emission by using time series data of forty-four years from 1972-2015. The performance of these developed models was assessed with the help of different selection measure criteria and the model having minimum value of these criteria considered as the best forecasting model. Based on findings, it has been observed that out of different ARIMA models, ARIMA (0, 2, 1) is the best fitted model in predicting the emission of carbon dioxide in Bangladesh. Using this best fitted model, the forecasted value of carbon dioxide emission in Bangladesh, for the year 2016, 2017 and 2018 as obtained from ARIMA (0, 2, 1) was obtained as 83.94657 Metric Tons, 89.90464 Metric Tons and 96.28557 Metric Tons respectively.展开更多
A child born with untreatable birth defect encounters constant challenges in lifetime. Spina bifida is such type of defect mainly affecting neural tube. As a result, a child with spina bifida faces abnormal physical a...A child born with untreatable birth defect encounters constant challenges in lifetime. Spina bifida is such type of defect mainly affecting neural tube. As a result, a child with spina bifida faces abnormal physical appearance to neurological dysfunctions. The incident rate of such birth defect is relatively common compared to other birth defects, therefore, an awareness among people is necessary to avoid such vulnerability. Therefore, this article provides a general outline of symptoms, types, risk factors, pathophysiology, preventive and therapeutic strategies of spina bifida which will help the general people for better understanding of the disease and be able to take precautions to combat such defect.展开更多
The present study is focused on multi-objective performance optimization&thermodynamic analysis from the perspectives of energy and exergy for Recompression,Partial Cooling&Main Compression Intercooling superc...The present study is focused on multi-objective performance optimization&thermodynamic analysis from the perspectives of energy and exergy for Recompression,Partial Cooling&Main Compression Intercooling supercritical CO_(2)(sCO_(2))Brayton cycles for concentrated solar power(CSP)applications using machine learning algorithms.The novelty of this work lies in the integration of artificial neural networks(ANN)and genetic algorithms(GA)for optimizing the performance of advanced sCO_(2)power cycles considering climatic variation,which has significant implications for both the scientific community and engineering applications in the renewable energy sector.The methodology employed includes thermodynamic analysis based on energy,exergy&environmental factors including system performance optimization.The system is modelled for net power production of 15 MW thermal output utilizing equations for the energy and exergy balance for each component.Subsequently,thermodynamic model extracted dataset used for prediction&evaluation of Random Forest,XGBoost,KNN,AdaBoost,ANN and LightGBM algorithm.Finally,considering climate conditions,multi-objective optimization is carried out for the CSP integrated sCO_(2)Power cycle for optimal power output,exergy destruction,thermal and exergetic efficiency.Genetic algorithm and TOPSIS(technique for order of preference by similarity to ideal solution),multi-objective decision-making tool,were used to determine the optimum operating conditions.The major findings of this work reveal significant improvements in the performance of the advanced sCO_(2)cycle by 1.68%and 7.87%compared to conventional recompression and partial cooling cycle,respectively.This research could advance renewable energy technologies,particularly concentrated solar power,by improving power cycle designs to increase system efficiency and economic feasibility.Optimized advanced supercritical CO_(2)power cycles in concentrated solar power plants might increase renewable energy use and energy generation infrastructure,potentially opening new research avenues.展开更多
文摘A secured and scalable Peer-to-Peer (P2P) energy trading platform can facilitate the integration of renewable energy and thus contribute to building sustainable energy infrastructure. The decentralized architecture of blockchain makes it a befitting candidate to actualize an efficient P2P energy trading market. However, for a sustainable and dynamic blockchain-based P2P energy trading platform, few critical aspects such as security, privacy and scalability need to be addressed with high priority. This paper proposes a blockchain-based solution for energy trading among the consumers which ensures the systems’ security, protects users’ privacy, and improves the overall scalability. More specifically, we develop a multilayered semi-permissioned blockchain-based platform to facilitate energy transactions. The practical byzantine fault tolerant algorithm is employed as the underlying consensus for verification and validation of transactions which ensures the system’s tolerance against internal error and malicious attacks. Additionally, we introduce the idea of quality of transaction (QoT)—a reward system for the participants of the network that eventually helps determine the participant’s eligibility for future transactions. The resiliency of the framework against the transaction malleability attack is demonstrated with two uses cases. Finally, a qualitative analysis is presented to indicate the system’s usefulness in improving the overall security, privacy, and scalability of the network.
文摘A novel microgrid control strategy is presented in this paper. A resilient community microgrid model, which is equipped with solar PV generation and electric vehicles (EVs) and an improved inverter control system, is considered. To fully exploit the capability of the community microgrid to operate in either grid-connected mode or islanded mode, as well as to achieve improved stability of the microgrid system, universal droop control, virtual inertia control, and a reinforcement learning-based control mechanism are combined in a cohesive manner, in which adaptive control parameters are determined online to tune the influence of the controllers. The microgrid model and control mechanisms are implemented in MATLAB/Simulink and set up in real-time simulation to test the feasibility and effectiveness of the proposed model. Experiment results reveal the effectiveness of regulating the controller’s frequency and voltage for various operating conditions and scenarios of a microgrid.
文摘In the present paper, different Autoregressive Integrated Moving Average (ARIMA) models were developed to model the carbon dioxide emission by using time series data of forty-four years from 1972-2015. The performance of these developed models was assessed with the help of different selection measure criteria and the model having minimum value of these criteria considered as the best forecasting model. Based on findings, it has been observed that out of different ARIMA models, ARIMA (0, 2, 1) is the best fitted model in predicting the emission of carbon dioxide in Bangladesh. Using this best fitted model, the forecasted value of carbon dioxide emission in Bangladesh, for the year 2016, 2017 and 2018 as obtained from ARIMA (0, 2, 1) was obtained as 83.94657 Metric Tons, 89.90464 Metric Tons and 96.28557 Metric Tons respectively.
文摘A child born with untreatable birth defect encounters constant challenges in lifetime. Spina bifida is such type of defect mainly affecting neural tube. As a result, a child with spina bifida faces abnormal physical appearance to neurological dysfunctions. The incident rate of such birth defect is relatively common compared to other birth defects, therefore, an awareness among people is necessary to avoid such vulnerability. Therefore, this article provides a general outline of symptoms, types, risk factors, pathophysiology, preventive and therapeutic strategies of spina bifida which will help the general people for better understanding of the disease and be able to take precautions to combat such defect.
文摘The present study is focused on multi-objective performance optimization&thermodynamic analysis from the perspectives of energy and exergy for Recompression,Partial Cooling&Main Compression Intercooling supercritical CO_(2)(sCO_(2))Brayton cycles for concentrated solar power(CSP)applications using machine learning algorithms.The novelty of this work lies in the integration of artificial neural networks(ANN)and genetic algorithms(GA)for optimizing the performance of advanced sCO_(2)power cycles considering climatic variation,which has significant implications for both the scientific community and engineering applications in the renewable energy sector.The methodology employed includes thermodynamic analysis based on energy,exergy&environmental factors including system performance optimization.The system is modelled for net power production of 15 MW thermal output utilizing equations for the energy and exergy balance for each component.Subsequently,thermodynamic model extracted dataset used for prediction&evaluation of Random Forest,XGBoost,KNN,AdaBoost,ANN and LightGBM algorithm.Finally,considering climate conditions,multi-objective optimization is carried out for the CSP integrated sCO_(2)Power cycle for optimal power output,exergy destruction,thermal and exergetic efficiency.Genetic algorithm and TOPSIS(technique for order of preference by similarity to ideal solution),multi-objective decision-making tool,were used to determine the optimum operating conditions.The major findings of this work reveal significant improvements in the performance of the advanced sCO_(2)cycle by 1.68%and 7.87%compared to conventional recompression and partial cooling cycle,respectively.This research could advance renewable energy technologies,particularly concentrated solar power,by improving power cycle designs to increase system efficiency and economic feasibility.Optimized advanced supercritical CO_(2)power cycles in concentrated solar power plants might increase renewable energy use and energy generation infrastructure,potentially opening new research avenues.
