Nodal pricing is a critical mechanism in electricity markets,utilized to determine the cost of power transmission to various nodes within a distribution network.As power systems evolve to incorporate higher levels of ...Nodal pricing is a critical mechanism in electricity markets,utilized to determine the cost of power transmission to various nodes within a distribution network.As power systems evolve to incorporate higher levels of renewable energy and face increasing demand fluctuations,traditional nodal pricing models often fall short to meet these new challenges.This research introduces a novel enhanced nodal pricing mechanism for distribution networks,integrating advanced optimization techniques and hybrid models to overcome these limitations.The primary objective is to develop a model that not only improves pricing accuracy but also enhances operational efficiency and system reliability.This study leverages cutting-edge hybrid algorithms,combining elements of machine learning with conventional optimization methods,to achieve superior performance.Key findings demonstrate that the proposed hybrid nodal pricing model significantly reduces pricing errors and operational costs compared to conventional methods.Through extensive simulations and comparative analysis,the model exhibits enhanced performance under varying load conditions and increased levels of renewable energy integration.The results indicate a substantial improvement in pricing precision and network stability.This study contributes to the ongoing discourse on optimizing electricity market mechanisms and provides actionable insights for policymakers and utility operators.By addressing the complexities of modern power distribution systems,our research offers a robust solution that enhances the efficiency and reliability of power distribution networks,marking a significant advancement in the field.展开更多
The literature on multi-attribute optimization for renewable energy source(RES)placement in deregulated power markets is extensive and diverse in methodology.This study focuses on the most relevant publications direct...The literature on multi-attribute optimization for renewable energy source(RES)placement in deregulated power markets is extensive and diverse in methodology.This study focuses on the most relevant publications directly addressing the research problem at hand.Similarly,while the body of work on optimal location and sizing of renewable energy generators(REGs)in balanced distribution systems is substantial,only the most pertinent sources are cited,aligning closely with the study’s objective function.A comprehensive literature review reveals several key research areas:RES integration,RES-related optimization techniques,strategic placement of wind and solar generation,and RES promotion in deregulated powermarkets,particularly within transmission systems.Furthermore,the optimal location and sizing of REGs in both balanced and unbalanced distribution systems have been extensively studied.RESs demonstrate significant potential for standalone applications in remote areas lacking conventional transmission and distribution infrastructure.Also presents a thorough review of current modeling and optimization approaches for RES-based distribution system location and sizing.Additionally,it examines the optimal positioning,sizing,and performance of hybrid and standalone renewable energy systems.This paper provides a comprehensive review of current modeling and optimization approaches for the location and sizing of Renewable Energy Sources(RESs)in distribution systems,focusing on both balanced and unbalanced networks.展开更多
Electric Vehicles(EVs)have emerged as a cleaner,low-carbon,and environmentally friendly alternative to traditional internal combustion engine(ICE)vehicles.With the increasing adoption of EVs,they are expected to event...Electric Vehicles(EVs)have emerged as a cleaner,low-carbon,and environmentally friendly alternative to traditional internal combustion engine(ICE)vehicles.With the increasing adoption of EVs,they are expected to eventually replace ICE vehicles entirely.However,the rapid growth of EVs has significantly increased energy demand,posing challenges for power grids and infrastructure.This surge in energy demand has driven advancements in developing efficient charging infrastructure and energy management solutions to mitigate the risks of power outages and disruptions caused by the rising number of EVs on the road.To address these challenges,various deep learning(DL)models,such as Recurrent Neural Networks(RNNs)and Long Short-Term Memory(LSTM)networks,have been employed for predicting energy demand at EV charging stations(EVCS).However,these models face certain limitations.They often lack interpretability,treating all input steps equally without assigning greater importance to critical patterns that are more relevant for prediction.Additionally,these models process data sequentially,which makes them computationally slower and less efficient when dealing with large datasets.In the context of these limitations,this paper introduces a novel Attention-Augmented Long Short-Term Memory(AA-LSTM)model.The proposed model integrates an attention mechanism to focus on the most relevant time steps,thereby enhancing its ability to capture long-term dependencies and improve prediction accuracy.By combining the strengths of LSTM networks in handling sequential data with the interpretability and efficiency of the attention mechanism,the AA-LSTM model delivers superior performance.The attention mechanism selectively prioritizes critical parts of the input sequence,reducing the computational burden and making the model faster and more effective.The AA-LSTM model achieves impressive results,demonstrating a Mean Absolute Percentage Error(MAPE)of 3.90%and a Mean Squared Error(MSE)of 0.40,highlighting its accuracy and reliability.These results suggest that the AA-LSTM model is a highly promising solution for predicting energy demand at EVCS,offering improved performance and efficiency compared to contemporary approaches.展开更多
Mobile banking security has witnessed significant R&D attention from both financial institutions and academia.This is due to the growing number of mobile baking applications and their reachability and usefulness t...Mobile banking security has witnessed significant R&D attention from both financial institutions and academia.This is due to the growing number of mobile baking applications and their reachability and usefulness to society.However,these applications are also attractive prey for cybercriminals,who use a variety of malware to steal personal banking information.Related literature in mobile banking security requiresmany permissions that are not necessary for the application’s intended security functionality.In this context,this paper presents a novel efficient permission identification approach for securing mobile banking(MoBShield)to detect and prevent malware.A permission-based dataset is generated for mobile banking malware detection that consists large number of malicious adware apps and benign apps to use as training datasets.The dataset is generated from 1650 malicious banking apps of the Canadian Institute of Cybersecurity,University of New Brunswick and benign apps from Google Play.A machine learning algorithm is used to determine whether amobile banking application ismalicious based on its permission requests.Further,an eXplainable machine learning(XML)approach is developed to improve trust by explaining the reasoning behind the algorithm’s behaviour.Performance evaluation tests that the approach can effectively and practically identify mobile banking malware with high precision and reduced false positives.Specifically,the adapted artificial neural networks(ANN),convolutional neural networks(CNN)and XML approaches achieve a higher accuracy of 99.7%and the adapted deep neural networks(DNN)approach achieves 99.6%accuracy in comparison with the state-of-the-art approaches.These promising results position the proposed approach as a potential tool for real-world scenarios,offering a robustmeans of identifying and thwarting malware inmobile-based banking applications.Consequently,MoBShield has the potential to significantly enhance the security and trustworthiness of mobile banking platforms,mitigating the risks posed by cyber threats and ensuring a safer user experience.展开更多
文摘Nodal pricing is a critical mechanism in electricity markets,utilized to determine the cost of power transmission to various nodes within a distribution network.As power systems evolve to incorporate higher levels of renewable energy and face increasing demand fluctuations,traditional nodal pricing models often fall short to meet these new challenges.This research introduces a novel enhanced nodal pricing mechanism for distribution networks,integrating advanced optimization techniques and hybrid models to overcome these limitations.The primary objective is to develop a model that not only improves pricing accuracy but also enhances operational efficiency and system reliability.This study leverages cutting-edge hybrid algorithms,combining elements of machine learning with conventional optimization methods,to achieve superior performance.Key findings demonstrate that the proposed hybrid nodal pricing model significantly reduces pricing errors and operational costs compared to conventional methods.Through extensive simulations and comparative analysis,the model exhibits enhanced performance under varying load conditions and increased levels of renewable energy integration.The results indicate a substantial improvement in pricing precision and network stability.This study contributes to the ongoing discourse on optimizing electricity market mechanisms and provides actionable insights for policymakers and utility operators.By addressing the complexities of modern power distribution systems,our research offers a robust solution that enhances the efficiency and reliability of power distribution networks,marking a significant advancement in the field.
文摘The literature on multi-attribute optimization for renewable energy source(RES)placement in deregulated power markets is extensive and diverse in methodology.This study focuses on the most relevant publications directly addressing the research problem at hand.Similarly,while the body of work on optimal location and sizing of renewable energy generators(REGs)in balanced distribution systems is substantial,only the most pertinent sources are cited,aligning closely with the study’s objective function.A comprehensive literature review reveals several key research areas:RES integration,RES-related optimization techniques,strategic placement of wind and solar generation,and RES promotion in deregulated powermarkets,particularly within transmission systems.Furthermore,the optimal location and sizing of REGs in both balanced and unbalanced distribution systems have been extensively studied.RESs demonstrate significant potential for standalone applications in remote areas lacking conventional transmission and distribution infrastructure.Also presents a thorough review of current modeling and optimization approaches for RES-based distribution system location and sizing.Additionally,it examines the optimal positioning,sizing,and performance of hybrid and standalone renewable energy systems.This paper provides a comprehensive review of current modeling and optimization approaches for the location and sizing of Renewable Energy Sources(RESs)in distribution systems,focusing on both balanced and unbalanced networks.
基金supported by the SC&SS,Jawaharlal Nehru University,New Delhi,India.
文摘Electric Vehicles(EVs)have emerged as a cleaner,low-carbon,and environmentally friendly alternative to traditional internal combustion engine(ICE)vehicles.With the increasing adoption of EVs,they are expected to eventually replace ICE vehicles entirely.However,the rapid growth of EVs has significantly increased energy demand,posing challenges for power grids and infrastructure.This surge in energy demand has driven advancements in developing efficient charging infrastructure and energy management solutions to mitigate the risks of power outages and disruptions caused by the rising number of EVs on the road.To address these challenges,various deep learning(DL)models,such as Recurrent Neural Networks(RNNs)and Long Short-Term Memory(LSTM)networks,have been employed for predicting energy demand at EV charging stations(EVCS).However,these models face certain limitations.They often lack interpretability,treating all input steps equally without assigning greater importance to critical patterns that are more relevant for prediction.Additionally,these models process data sequentially,which makes them computationally slower and less efficient when dealing with large datasets.In the context of these limitations,this paper introduces a novel Attention-Augmented Long Short-Term Memory(AA-LSTM)model.The proposed model integrates an attention mechanism to focus on the most relevant time steps,thereby enhancing its ability to capture long-term dependencies and improve prediction accuracy.By combining the strengths of LSTM networks in handling sequential data with the interpretability and efficiency of the attention mechanism,the AA-LSTM model delivers superior performance.The attention mechanism selectively prioritizes critical parts of the input sequence,reducing the computational burden and making the model faster and more effective.The AA-LSTM model achieves impressive results,demonstrating a Mean Absolute Percentage Error(MAPE)of 3.90%and a Mean Squared Error(MSE)of 0.40,highlighting its accuracy and reliability.These results suggest that the AA-LSTM model is a highly promising solution for predicting energy demand at EVCS,offering improved performance and efficiency compared to contemporary approaches.
基金the Deanship of Scientific Research(DSR),King Khalid University,Abha,under Grant No.RGP.1/260/45The author,therefore,gratefully acknowledges the DSR’s technical and financial support.
文摘Mobile banking security has witnessed significant R&D attention from both financial institutions and academia.This is due to the growing number of mobile baking applications and their reachability and usefulness to society.However,these applications are also attractive prey for cybercriminals,who use a variety of malware to steal personal banking information.Related literature in mobile banking security requiresmany permissions that are not necessary for the application’s intended security functionality.In this context,this paper presents a novel efficient permission identification approach for securing mobile banking(MoBShield)to detect and prevent malware.A permission-based dataset is generated for mobile banking malware detection that consists large number of malicious adware apps and benign apps to use as training datasets.The dataset is generated from 1650 malicious banking apps of the Canadian Institute of Cybersecurity,University of New Brunswick and benign apps from Google Play.A machine learning algorithm is used to determine whether amobile banking application ismalicious based on its permission requests.Further,an eXplainable machine learning(XML)approach is developed to improve trust by explaining the reasoning behind the algorithm’s behaviour.Performance evaluation tests that the approach can effectively and practically identify mobile banking malware with high precision and reduced false positives.Specifically,the adapted artificial neural networks(ANN),convolutional neural networks(CNN)and XML approaches achieve a higher accuracy of 99.7%and the adapted deep neural networks(DNN)approach achieves 99.6%accuracy in comparison with the state-of-the-art approaches.These promising results position the proposed approach as a potential tool for real-world scenarios,offering a robustmeans of identifying and thwarting malware inmobile-based banking applications.Consequently,MoBShield has the potential to significantly enhance the security and trustworthiness of mobile banking platforms,mitigating the risks posed by cyber threats and ensuring a safer user experience.
