The global transition towards sustainable transportation solutions has spurred the rapid growth o... more The global transition towards sustainable transportation solutions has spurred the rapid growth of electric vehicles (EVs) as a promising alternative to traditional internal combustion engine vehicles. However, as the adoption of EVs continues to accelerate, the focus has shifted towards ensuring the longevity of electric vehicle operations worldwide. This abstract aims to provide an in-depth exploration of the multifaceted aspects surrounding the longevity of EV operations on a global scale. The longevity of electric vehicle operations encompasses various dimensions, including technological advancements, infrastructure development, policy support, and consumer behavior. Firstly, advancements in battery technology play a pivotal role in determining the lifespan of EVs. The abstract delves into the evolution of battery chemistries, energy densities, and thermal management systems, which collectively impact battery life and overall vehicle longevity. Additionally, insights into batter...
As machine learning (ML) technology rapidly evolves, ML-based Intrusion Detection Systems (IDSs) ... more As machine learning (ML) technology rapidly evolves, ML-based Intrusion Detection Systems (IDSs) are increasingly utilized to safeguard networks from various cyber threats. However, a significant challenge arises from adversarial example (AE) attacks, where slight modifications (such as minor increases in packet inter-arrival times) can mislead a well-trained IDS into incorrect predictions. To address this issue, we introduce KUNDA, an AE detection system that leverages manifold and decision boundary characteristics. Our approach is based on two key observations: (1) AEs are typically located close to their original data manifold, regardless of their misclassification, and (2) AEs are often situated near decision boundaries to minimize perturbation. KUNDA detects AEs by analyzing discrepancies between manifold assessments and IDS model predictions, as well as evaluating model uncertainty in response to minor perturbations. We tested KUNDA on binary and multi-class IDSs using two datasets (NSL-KDD and CICIDS) under three advanced AE attacks. The results indicate that KUNDA achieves a high true-positive rate (98.41%) with a 5% false-positive rate.
International Journal of Scientific and Engineering Research, Mar 15, 2013
This paper discusses various aspects of multiple FACTS devices of control modes and settings and ... more This paper discusses various aspects of multiple FACTS devices of control modes and settings and evaluates their impacts on the power system reliability. Two UPFC’s are used for the reliability evaluation in a test system. Multiple UPFC’s can control various power system parameters, such as bus voltages, reactive power and line flows effectively. The impact of UPFC control modes and settings on the power system reliability has not been addressed sufficiently yet. The various control modes of UPFC and the optimal settings of UPFC with respect to reliability is proposed. The remedial action cost (RAC) can be minimized associated with the reliability index evaluation. The proposed method is applied to the IEEE nine bus system in this paper. The performance of multiple UPFC’s also analysed in detail.
Computer Methods in Biomechanics and Biomedical Engineering, Nov 29, 2023
A classification framework for hand gestures using Electromyography (EMG) signals in prosthetic h... more A classification framework for hand gestures using Electromyography (EMG) signals in prosthetic hands is presented. Leveraging the multi-scale characteristics and temporal nature of EMG signals, a Convolutional Neural Network (CNN) is used to extract multi-scale features and classify them with spatial-temporal attention. A multi-scale coarse-grained layer introduced into the input of one-dimensional CNN (1D-CNN) facilitates multi-scale feature extraction. The multi-scale features are fed into the attention layer and subsequently given to the fully connected layer to perform classification. The proposed model achieves classification accuracies of 93.4%, 92.8%, 91.3%, and 94.1% for Ninapro DB1, DB2, DB5, and DB7 respectively, thereby enhancing the confidence of prosthetic hand users.
Unlike traditional reliability analysis in power systems, which focuses on safely and securely wi... more Unlike traditional reliability analysis in power systems, which focuses on safely and securely withstanding credible contingencies during daily operations, resilience assessments are concerned with high-impact, low-probability (HILP) events in the grid. This paper proposes an autonomous load restoration architecture based on the IEC 61850-8-1 GOOSE communication protocol to enhance feeder-level resilience in active power distribution grids. Unlike previous research on outage management, which often lacks a focus on resilience, offers reactive solutions to local single-fault events, and does not fully utilize both network built-in flexibilities and flexible resources, this proposed solution leverages in Imported power and flexibility from neighboring networks, Distributed energy resources and Vehicle-to-grid capacity of electric vehicle aggregations. These elements enhance feederlevel resourcefulness for agile response and recovery. The proposed solution employs real-time selfreconfiguration strategies to manage both single and subsequent outage events, thereby improving resilience before and during contingency periods. Additionally, a resilience evaluation framework that quantifies the contributions of all resources involved in service restoration is developed.
As electric vehicles become increasingly prevalent, ensuring the reliability and longevity of the... more As electric vehicles become increasingly prevalent, ensuring the reliability and longevity of their battery systems is paramount. This study presents a comprehensive evaluation of battery reliability in EVs through a detailed case study. By analyzing real-world data from a fleet of electric vehicles over a significant period, we assess key parameters such as state of charge (SOC), state of health (SOH), and degradation patterns. Advanced modeling techniques, including electrochemical, equivalent circuit, and data-driven models, are employed to provide a holistic view of battery performance. The findings highlight critical factors influencing battery reliability, including environmental conditions, driving habits, and charging practices. This case study offers valuable insights into the challenges and best practices for enhancing battery longevity and reliability in electric vehicles, contributing to the development of more robust and efficient EV battery management systems.
The rapid advancement of electric vehicles (EVs) hinges on the development of sophisticated batte... more The rapid advancement of electric vehicles (EVs) hinges on the development of sophisticated battery technologies and management systems. This paper explores novel battery modeling techniques that enhance the performance, reliability, and longevity of batteries in EVs. We examine various approaches, including electrochemical models, equivalent circuit models (ECMs), data-driven models, hybrid models, and physics-based reduced order models (ROMs). Electrochemical models provide in-depth insights into internal battery processes, while ECMs offer simplified yet effective representations of battery behavior. Data-driven models leverage machine learning and big data to predict battery performance, and hybrid models combine multiple approaches for comprehensive modeling. ROMs simplify complex models to facilitate real-time applications. By integrating these techniques, researchers and engineers can optimize battery management systems, ultimately contributing to the increased efficiency and sustainability of electric vehicles. This paper aims to highlight the potential and applications of these innovative modeling techniques in advancing EV battery technology.
The integration of renewable energy sources (RES) and electric vehicles (EVs) into microgrids (MG... more The integration of renewable energy sources (RES) and electric vehicles (EVs) into microgrids (MGs) holds significant potential for enhancing energy resilience, addressing environmental concerns, and promoting decentralized energy systems. This survey paper offers a comprehensive discussion on improving MG operation through EV integration. It evaluates the current status of EV integration into MGs, focusing on technological advancements and emerging trends while identifying key technical challenges and opportunities. Additionally, this paper examines the crucial role of EVs in participating in vehicle-to-grid (V2G) services, which provide ancillary support to improve MG performance. The importance of a reliable communication infrastructure for effective information exchange between EVs, EV charging stations (EVCSs), and MGs is emphasized for the successful implementation of V2G services. The discussion extends to the contributions of EVs to primary, secondary, and tertiary MG controls. The paper also analyzes the integration of EVs into both AC and DC MGs and proposes configurations for each. Finally, the paper concludes with recommendations for future research to unlock the full potential of EV contributions to MG performance, thereby advancing sustainable and resilient energy systems. Key findings include solutions for MG voltage and frequency regulation via EV bidirectional converter power flow control, EV charger configurations for integration into AC and DC MGs, the role of EVs in enhancing MG operational resilience and adaptability, and the challenges arising from V2G implementation in such systems.
The escalation of pollution levels, contributing to greenhouse gas emissions and exacerbating glo... more The escalation of pollution levels, contributing to greenhouse gas emissions and exacerbating global warming, is expected to drive the proliferation of Electric Vehicles (EVs). As EVs increasingly interface with the electrical grid, they are poised to exert significant influence over voltage profiles and grid loads. This study focuses on modeling and analyzing the integration of renewable energy sources and EVs within microgrid environments. Key components of the microgrid studied include a diesel generator serving as the primary power source, coupled with a Photovoltaic (PV) farm and wind farm for renewable electricity generation. Additionally, a Vehicle-to-Grid (V2G) system, strategically placed near the microgrid's load, enhances flexibility in managing EV charging and discharging cycles. The growing capacity of these renewable sources underscores the pivotal role of microgrids in meeting diverse energy demands, from institutions like hospitals and universities to EV charging stations, as well as broader community and industrial energy needs. Charging infrastructure plays a critical role in sustaining EV operations, affecting grid stability and energy management strategies. This paper investigates the dynamic impact of EV integration on microgrid networks, taking into account the nonlinear circuit components inherent in EV systems. Furthermore, it presents detailed modeling and analysis of how renewable energy sources and EV integration can optimize microgrid performance and contribute to sustainable energy solutions.
Electric vehicles (EVs) have emerged as a compelling alternative to traditional fossil fuel vehic... more Electric vehicles (EVs) have emerged as a compelling alternative to traditional fossil fuel vehicles, offering notable advantages in terms of carbon neutrality and environmental sustainability within the contemporary transportation sector. The widespread adoption of EVs has triggered a surge in demand for charging infrastructure. However, the scarcity of charging stations (CSs) poses challenges to ensuring efficient and dependable EV charging services. Previous research has primarily focused on predicting EV energy consumption at charging stations without thoroughly examining the various influencing factors such as energy demand, weather conditions, and time of day. To address this gap, we propose an energy consumption and distribution framework tailored for EVs within a smart grid environment, aimed at optimizing EV charging efficiency. Our framework conducts a comprehensive analysis of influencing parameters including location, weekday/weekend differentiations, and user behavior. Leveraging EV dataset, we delve into detailed insights into energy consumption patterns based on specific parameters such as CS location (Station ID), overall location (Location ID), weekdays, weekends, and user profiles (UserID). The primary objective of our study is to gain insights into smart grid-enabled electricity distribution to charging stations by examining energy consumption patterns, thus ensuring reliable EV charging services. We employ various analytical methods to scrutinize the impact of different parameters and present our findings through graphical representations, offering a nuanced understanding of the dynamics involved.
In this study, we address the optimal scheduling of electric vehicle (EV) charging at a multi-poi... more In this study, we address the optimal scheduling of electric vehicle (EV) charging at a multi-point charging station equipped with renewable energy sources and grid energy access. We model the uncertainty of EV arrivals, intermittent renewable energy generation, and fluctuating grid power prices as independent Markov processes. Additionally, the required charging energy for each EV is stochastic. Our objective is to minimize the average waiting time for EVs while adhering to long-term cost constraints. We introduce a queue mapping technique to transform the EV queue into a charging demand queue, establishing equivalence in minimizing their average lengths. We then concentrate on minimizing the charging demand queue length under the long-term cost constraint using a Markov decision process (MDP) framework. The system state encompasses the charging demand queue length, EV arrival patterns, renewable energy storage battery levels, renewable energy arrivals, and grid power prices. Our proposed 2-D policy involves decisions regarding the number of charging demands and energy allocation from the storage battery. We derive necessary conditions for the optimal policy and explore reducing the 2-D policy to focus solely on the number of charging demands. Furthermore, we identify optimal scenarios for charging no demand or maximizing charging demand based on defined sets of system states.
Examining the global evolution of renewable electricity production entails a comprehensive analys... more Examining the global evolution of renewable electricity production entails a comprehensive analysis of various sources, among which solar energy plays a pivotal role. As nations worldwide strive to transition towards sustainable energy solutions, understanding the significance of solar power in this trajectory becomes imperative. Solar energy, with its abundant and inexhaustible nature, offers immense potential to mitigate carbon emissions and reduce reliance on fossil fuels. By harnessing the power of sunlight, countries can diversify their energy portfolios, enhance energy security, and foster economic growth through job creation and technological innovation. Therefore, closely observing the progression of renewable electricity generation while assessing the impact and utilization of solar energy is essential for shaping a greener and more sustainable future.
As the integration of variable renewable energy (VRE), such as wind and solar, continues to escal... more As the integration of variable renewable energy (VRE), such as wind and solar, continues to escalate within electric power systems, a host of technical challenges emerges, primarily centered around the imperative to uphold equilibrium between electricity demand and generation across all timescales. This paper delves into the intricacies of integrating ultra-high levels of VRE into electric power systems, scrutinizing the challenges inherent in this endeavor. Furthermore, it offers an extensive review of potential solutions to these challenges, ranging from technological advancements to regulatory frameworks. Additionally, this paper highlights several noteworthy examples of ultra-high VRE systems currently operational, shedding light on their design, implementation, and performance within real-world contexts. Through comprehensive analysis and examination, this paper aims to provide insights into the complexities and opportunities associated with the integration of VRE into modern electric power systems.
Introducing an innovative solar-aided power generation system that optimizes both non-concentrati... more Introducing an innovative solar-aided power generation system that optimizes both non-concentrating and concentrating solar energy for lignite drying. This system employs a two-stage solar drying process, efficiently utilizing solar energy in a cascade manner. Notably, a cost-effective non-concentrating solar collector maximizes energy conversion by capturing diffused sunlight during lignite drying. Integration of solar power increases electric output by 26.8 MWe for a 600-MW base unit, achieving a remarkable 35.83% conversion efficiency. Enhanced thermal performance is facilitated by the system's low-cost nonconcentrating solar collector. Financially, it generates an annual revenue of 63.45 million CNY, with solar-generated electricity costing 0.346 CNY/kWh. Furthermore, solar fluctuation is addressed through dried coal storage, ensuring consistent performance under varying conditions. This system stands out for its efficient utilization of diffused solar irradiance, offering superior efficiency and economic viability compared to conventional methods.
This paper investigates the impact of green finance on carbon emissions in India, with a focus on... more This paper investigates the impact of green finance on carbon emissions in India, with a focus on optimizing energy consumption. The study begins by examining the theoretical perspective to understand the mechanism and pathway through which green finance influences carbon emissions, while also analyzing the role of energy consumption in this process. To empirically analyze the influence of green finance on carbon emissions, the paper employs the STIRPAT model, chain multiple mediation effect model, and panel threshold model using provincial data from India spanning 2017 to 2022. Green finance has a significant and consistent reduction effect on carbon emissions. This conclusion holds even after accounting for potential endogeneity. The analysis of regional heterogeneity reveals that the inhibitory effect is particularly notable in northern regions, high-carbon emission regions, and energy-rich regions. The results of the bootstrap test indicate that, at the national level, green finance reduces carbon emissions through three distinct paths: green technological innovation, ecological evolution of the industrial structure, and green technological innovation facilitating the ecological evolution of the industrial structure. Moreover, in energy-rich regions, green finance significantly inhibits carbon emissions through all three paths, while in energy-poor regions, green finance only reduces carbon emissions through green technological innovation.
This research paper explores different aspects related to the utilization of multiple FACTS devic... more This research paper explores different aspects related to the utilization of multiple FACTS devices, specifically focusing on control modes, settings, and their impact on power system reliability. In the evaluation of power system reliability, two UPFCs are employed within a test system. By employing multiple UPFCs, it becomes possible to effectively control various power system parameters, including bus voltages, reactive power, and line flows. However, the influence of UPFC control modes and settings on power system reliability has not been adequately addressed thus far. To address this gap, the paper proposes various control modes for UPFC and suggests optimal settings that enhance reliability. Furthermore, a reliability index evaluation is performed with the objective of minimizing the associated remedial action cost (RAC). The proposed methodology is applied and tested on the IEEE nine bus system, providing a comprehensive analysis of the performance of multiple UPFCs.
The inspiration behind this research paper stems from a sense of social responsibility to mitigat... more The inspiration behind this research paper stems from a sense of social responsibility to mitigate road accidents. Its primary objective is to enhance the safety and security of motorcycle riders by promoting helmet usage. The proposed system incorporates a mechanism that prevents the motorcycle from starting unless the rider wears a helmet and is not under the influence of alcohol. Furthermore, the system utilizes sensors to alert the rider of any obstacles approaching from the rear. In the unfortunate event of an accident, the GSM module promptly sends distress signals to the nearest police station, registered relatives, and other designated individuals.
Ensuring high-quality and reliable power supply to customers plays a crucial role in the power sy... more Ensuring high-quality and reliable power supply to customers plays a crucial role in the power system. Restructuring the power system is an efficient approach to evaluate and deliver economic and uninterrupted power supply. Reliability assessment, which depends on system security and adequacy, serves as a means to check the reliability of the power system. Sequential simulation is proposed as a reliable method to enhance power system performance indices and assess reliability in unbundled power utilities. By simulating the stochastic behavior and sequential process of the system, outage occurrences can be identified and reflected in the form of a reliability index. This paper presents an effective method to improve power system reliability in the transmission and distribution network.
This research study delves into the analysis of gender-based patterns in two-wheeler electric veh... more This research study delves into the analysis of gender-based patterns in two-wheeler electric vehicle (EV) riding with respect to accidents. As electric mobility gains momentum worldwide, it becomes essential to understand the potential differences in accident rates, risk factors, and safety practices between male and female riders. The objective of this research is to identify and compare accident trends, driving behaviors, and risk perceptions associated with male and female two-wheeler EV riders, providing valuable insights to enhance safety measures and promote gender-specific accident prevention strategies. The research employs a mixed-method approach, combining the analysis of historical accident data and surveys. Accident data are collected from reliable sources, including traffic authorities, police reports, and insurance records, to uncover patterns in accident occurrence, severity, and contributing factors specific to each gender. Additionally, surveys and questionnaires are distributed to representative samples of male and female electric two-wheeler riders to capture driving behaviors, safety practices, and accident experiences.
The global transition towards sustainable transportation solutions has spurred the rapid growth o... more The global transition towards sustainable transportation solutions has spurred the rapid growth of electric vehicles (EVs) as a promising alternative to traditional internal combustion engine vehicles. However, as the adoption of EVs continues to accelerate, the focus has shifted towards ensuring the longevity of electric vehicle operations worldwide. This abstract aims to provide an in-depth exploration of the multifaceted aspects surrounding the longevity of EV operations on a global scale. The longevity of electric vehicle operations encompasses various dimensions, including technological advancements, infrastructure development, policy support, and consumer behavior. Firstly, advancements in battery technology play a pivotal role in determining the lifespan of EVs. The abstract delves into the evolution of battery chemistries, energy densities, and thermal management systems, which collectively impact battery life and overall vehicle longevity. Additionally, insights into batter...
As machine learning (ML) technology rapidly evolves, ML-based Intrusion Detection Systems (IDSs) ... more As machine learning (ML) technology rapidly evolves, ML-based Intrusion Detection Systems (IDSs) are increasingly utilized to safeguard networks from various cyber threats. However, a significant challenge arises from adversarial example (AE) attacks, where slight modifications (such as minor increases in packet inter-arrival times) can mislead a well-trained IDS into incorrect predictions. To address this issue, we introduce KUNDA, an AE detection system that leverages manifold and decision boundary characteristics. Our approach is based on two key observations: (1) AEs are typically located close to their original data manifold, regardless of their misclassification, and (2) AEs are often situated near decision boundaries to minimize perturbation. KUNDA detects AEs by analyzing discrepancies between manifold assessments and IDS model predictions, as well as evaluating model uncertainty in response to minor perturbations. We tested KUNDA on binary and multi-class IDSs using two datasets (NSL-KDD and CICIDS) under three advanced AE attacks. The results indicate that KUNDA achieves a high true-positive rate (98.41%) with a 5% false-positive rate.
International Journal of Scientific and Engineering Research, Mar 15, 2013
This paper discusses various aspects of multiple FACTS devices of control modes and settings and ... more This paper discusses various aspects of multiple FACTS devices of control modes and settings and evaluates their impacts on the power system reliability. Two UPFC’s are used for the reliability evaluation in a test system. Multiple UPFC’s can control various power system parameters, such as bus voltages, reactive power and line flows effectively. The impact of UPFC control modes and settings on the power system reliability has not been addressed sufficiently yet. The various control modes of UPFC and the optimal settings of UPFC with respect to reliability is proposed. The remedial action cost (RAC) can be minimized associated with the reliability index evaluation. The proposed method is applied to the IEEE nine bus system in this paper. The performance of multiple UPFC’s also analysed in detail.
Computer Methods in Biomechanics and Biomedical Engineering, Nov 29, 2023
A classification framework for hand gestures using Electromyography (EMG) signals in prosthetic h... more A classification framework for hand gestures using Electromyography (EMG) signals in prosthetic hands is presented. Leveraging the multi-scale characteristics and temporal nature of EMG signals, a Convolutional Neural Network (CNN) is used to extract multi-scale features and classify them with spatial-temporal attention. A multi-scale coarse-grained layer introduced into the input of one-dimensional CNN (1D-CNN) facilitates multi-scale feature extraction. The multi-scale features are fed into the attention layer and subsequently given to the fully connected layer to perform classification. The proposed model achieves classification accuracies of 93.4%, 92.8%, 91.3%, and 94.1% for Ninapro DB1, DB2, DB5, and DB7 respectively, thereby enhancing the confidence of prosthetic hand users.
Unlike traditional reliability analysis in power systems, which focuses on safely and securely wi... more Unlike traditional reliability analysis in power systems, which focuses on safely and securely withstanding credible contingencies during daily operations, resilience assessments are concerned with high-impact, low-probability (HILP) events in the grid. This paper proposes an autonomous load restoration architecture based on the IEC 61850-8-1 GOOSE communication protocol to enhance feeder-level resilience in active power distribution grids. Unlike previous research on outage management, which often lacks a focus on resilience, offers reactive solutions to local single-fault events, and does not fully utilize both network built-in flexibilities and flexible resources, this proposed solution leverages in Imported power and flexibility from neighboring networks, Distributed energy resources and Vehicle-to-grid capacity of electric vehicle aggregations. These elements enhance feederlevel resourcefulness for agile response and recovery. The proposed solution employs real-time selfreconfiguration strategies to manage both single and subsequent outage events, thereby improving resilience before and during contingency periods. Additionally, a resilience evaluation framework that quantifies the contributions of all resources involved in service restoration is developed.
As electric vehicles become increasingly prevalent, ensuring the reliability and longevity of the... more As electric vehicles become increasingly prevalent, ensuring the reliability and longevity of their battery systems is paramount. This study presents a comprehensive evaluation of battery reliability in EVs through a detailed case study. By analyzing real-world data from a fleet of electric vehicles over a significant period, we assess key parameters such as state of charge (SOC), state of health (SOH), and degradation patterns. Advanced modeling techniques, including electrochemical, equivalent circuit, and data-driven models, are employed to provide a holistic view of battery performance. The findings highlight critical factors influencing battery reliability, including environmental conditions, driving habits, and charging practices. This case study offers valuable insights into the challenges and best practices for enhancing battery longevity and reliability in electric vehicles, contributing to the development of more robust and efficient EV battery management systems.
The rapid advancement of electric vehicles (EVs) hinges on the development of sophisticated batte... more The rapid advancement of electric vehicles (EVs) hinges on the development of sophisticated battery technologies and management systems. This paper explores novel battery modeling techniques that enhance the performance, reliability, and longevity of batteries in EVs. We examine various approaches, including electrochemical models, equivalent circuit models (ECMs), data-driven models, hybrid models, and physics-based reduced order models (ROMs). Electrochemical models provide in-depth insights into internal battery processes, while ECMs offer simplified yet effective representations of battery behavior. Data-driven models leverage machine learning and big data to predict battery performance, and hybrid models combine multiple approaches for comprehensive modeling. ROMs simplify complex models to facilitate real-time applications. By integrating these techniques, researchers and engineers can optimize battery management systems, ultimately contributing to the increased efficiency and sustainability of electric vehicles. This paper aims to highlight the potential and applications of these innovative modeling techniques in advancing EV battery technology.
The integration of renewable energy sources (RES) and electric vehicles (EVs) into microgrids (MG... more The integration of renewable energy sources (RES) and electric vehicles (EVs) into microgrids (MGs) holds significant potential for enhancing energy resilience, addressing environmental concerns, and promoting decentralized energy systems. This survey paper offers a comprehensive discussion on improving MG operation through EV integration. It evaluates the current status of EV integration into MGs, focusing on technological advancements and emerging trends while identifying key technical challenges and opportunities. Additionally, this paper examines the crucial role of EVs in participating in vehicle-to-grid (V2G) services, which provide ancillary support to improve MG performance. The importance of a reliable communication infrastructure for effective information exchange between EVs, EV charging stations (EVCSs), and MGs is emphasized for the successful implementation of V2G services. The discussion extends to the contributions of EVs to primary, secondary, and tertiary MG controls. The paper also analyzes the integration of EVs into both AC and DC MGs and proposes configurations for each. Finally, the paper concludes with recommendations for future research to unlock the full potential of EV contributions to MG performance, thereby advancing sustainable and resilient energy systems. Key findings include solutions for MG voltage and frequency regulation via EV bidirectional converter power flow control, EV charger configurations for integration into AC and DC MGs, the role of EVs in enhancing MG operational resilience and adaptability, and the challenges arising from V2G implementation in such systems.
The escalation of pollution levels, contributing to greenhouse gas emissions and exacerbating glo... more The escalation of pollution levels, contributing to greenhouse gas emissions and exacerbating global warming, is expected to drive the proliferation of Electric Vehicles (EVs). As EVs increasingly interface with the electrical grid, they are poised to exert significant influence over voltage profiles and grid loads. This study focuses on modeling and analyzing the integration of renewable energy sources and EVs within microgrid environments. Key components of the microgrid studied include a diesel generator serving as the primary power source, coupled with a Photovoltaic (PV) farm and wind farm for renewable electricity generation. Additionally, a Vehicle-to-Grid (V2G) system, strategically placed near the microgrid's load, enhances flexibility in managing EV charging and discharging cycles. The growing capacity of these renewable sources underscores the pivotal role of microgrids in meeting diverse energy demands, from institutions like hospitals and universities to EV charging stations, as well as broader community and industrial energy needs. Charging infrastructure plays a critical role in sustaining EV operations, affecting grid stability and energy management strategies. This paper investigates the dynamic impact of EV integration on microgrid networks, taking into account the nonlinear circuit components inherent in EV systems. Furthermore, it presents detailed modeling and analysis of how renewable energy sources and EV integration can optimize microgrid performance and contribute to sustainable energy solutions.
Electric vehicles (EVs) have emerged as a compelling alternative to traditional fossil fuel vehic... more Electric vehicles (EVs) have emerged as a compelling alternative to traditional fossil fuel vehicles, offering notable advantages in terms of carbon neutrality and environmental sustainability within the contemporary transportation sector. The widespread adoption of EVs has triggered a surge in demand for charging infrastructure. However, the scarcity of charging stations (CSs) poses challenges to ensuring efficient and dependable EV charging services. Previous research has primarily focused on predicting EV energy consumption at charging stations without thoroughly examining the various influencing factors such as energy demand, weather conditions, and time of day. To address this gap, we propose an energy consumption and distribution framework tailored for EVs within a smart grid environment, aimed at optimizing EV charging efficiency. Our framework conducts a comprehensive analysis of influencing parameters including location, weekday/weekend differentiations, and user behavior. Leveraging EV dataset, we delve into detailed insights into energy consumption patterns based on specific parameters such as CS location (Station ID), overall location (Location ID), weekdays, weekends, and user profiles (UserID). The primary objective of our study is to gain insights into smart grid-enabled electricity distribution to charging stations by examining energy consumption patterns, thus ensuring reliable EV charging services. We employ various analytical methods to scrutinize the impact of different parameters and present our findings through graphical representations, offering a nuanced understanding of the dynamics involved.
In this study, we address the optimal scheduling of electric vehicle (EV) charging at a multi-poi... more In this study, we address the optimal scheduling of electric vehicle (EV) charging at a multi-point charging station equipped with renewable energy sources and grid energy access. We model the uncertainty of EV arrivals, intermittent renewable energy generation, and fluctuating grid power prices as independent Markov processes. Additionally, the required charging energy for each EV is stochastic. Our objective is to minimize the average waiting time for EVs while adhering to long-term cost constraints. We introduce a queue mapping technique to transform the EV queue into a charging demand queue, establishing equivalence in minimizing their average lengths. We then concentrate on minimizing the charging demand queue length under the long-term cost constraint using a Markov decision process (MDP) framework. The system state encompasses the charging demand queue length, EV arrival patterns, renewable energy storage battery levels, renewable energy arrivals, and grid power prices. Our proposed 2-D policy involves decisions regarding the number of charging demands and energy allocation from the storage battery. We derive necessary conditions for the optimal policy and explore reducing the 2-D policy to focus solely on the number of charging demands. Furthermore, we identify optimal scenarios for charging no demand or maximizing charging demand based on defined sets of system states.
Examining the global evolution of renewable electricity production entails a comprehensive analys... more Examining the global evolution of renewable electricity production entails a comprehensive analysis of various sources, among which solar energy plays a pivotal role. As nations worldwide strive to transition towards sustainable energy solutions, understanding the significance of solar power in this trajectory becomes imperative. Solar energy, with its abundant and inexhaustible nature, offers immense potential to mitigate carbon emissions and reduce reliance on fossil fuels. By harnessing the power of sunlight, countries can diversify their energy portfolios, enhance energy security, and foster economic growth through job creation and technological innovation. Therefore, closely observing the progression of renewable electricity generation while assessing the impact and utilization of solar energy is essential for shaping a greener and more sustainable future.
As the integration of variable renewable energy (VRE), such as wind and solar, continues to escal... more As the integration of variable renewable energy (VRE), such as wind and solar, continues to escalate within electric power systems, a host of technical challenges emerges, primarily centered around the imperative to uphold equilibrium between electricity demand and generation across all timescales. This paper delves into the intricacies of integrating ultra-high levels of VRE into electric power systems, scrutinizing the challenges inherent in this endeavor. Furthermore, it offers an extensive review of potential solutions to these challenges, ranging from technological advancements to regulatory frameworks. Additionally, this paper highlights several noteworthy examples of ultra-high VRE systems currently operational, shedding light on their design, implementation, and performance within real-world contexts. Through comprehensive analysis and examination, this paper aims to provide insights into the complexities and opportunities associated with the integration of VRE into modern electric power systems.
Introducing an innovative solar-aided power generation system that optimizes both non-concentrati... more Introducing an innovative solar-aided power generation system that optimizes both non-concentrating and concentrating solar energy for lignite drying. This system employs a two-stage solar drying process, efficiently utilizing solar energy in a cascade manner. Notably, a cost-effective non-concentrating solar collector maximizes energy conversion by capturing diffused sunlight during lignite drying. Integration of solar power increases electric output by 26.8 MWe for a 600-MW base unit, achieving a remarkable 35.83% conversion efficiency. Enhanced thermal performance is facilitated by the system's low-cost nonconcentrating solar collector. Financially, it generates an annual revenue of 63.45 million CNY, with solar-generated electricity costing 0.346 CNY/kWh. Furthermore, solar fluctuation is addressed through dried coal storage, ensuring consistent performance under varying conditions. This system stands out for its efficient utilization of diffused solar irradiance, offering superior efficiency and economic viability compared to conventional methods.
This paper investigates the impact of green finance on carbon emissions in India, with a focus on... more This paper investigates the impact of green finance on carbon emissions in India, with a focus on optimizing energy consumption. The study begins by examining the theoretical perspective to understand the mechanism and pathway through which green finance influences carbon emissions, while also analyzing the role of energy consumption in this process. To empirically analyze the influence of green finance on carbon emissions, the paper employs the STIRPAT model, chain multiple mediation effect model, and panel threshold model using provincial data from India spanning 2017 to 2022. Green finance has a significant and consistent reduction effect on carbon emissions. This conclusion holds even after accounting for potential endogeneity. The analysis of regional heterogeneity reveals that the inhibitory effect is particularly notable in northern regions, high-carbon emission regions, and energy-rich regions. The results of the bootstrap test indicate that, at the national level, green finance reduces carbon emissions through three distinct paths: green technological innovation, ecological evolution of the industrial structure, and green technological innovation facilitating the ecological evolution of the industrial structure. Moreover, in energy-rich regions, green finance significantly inhibits carbon emissions through all three paths, while in energy-poor regions, green finance only reduces carbon emissions through green technological innovation.
This research paper explores different aspects related to the utilization of multiple FACTS devic... more This research paper explores different aspects related to the utilization of multiple FACTS devices, specifically focusing on control modes, settings, and their impact on power system reliability. In the evaluation of power system reliability, two UPFCs are employed within a test system. By employing multiple UPFCs, it becomes possible to effectively control various power system parameters, including bus voltages, reactive power, and line flows. However, the influence of UPFC control modes and settings on power system reliability has not been adequately addressed thus far. To address this gap, the paper proposes various control modes for UPFC and suggests optimal settings that enhance reliability. Furthermore, a reliability index evaluation is performed with the objective of minimizing the associated remedial action cost (RAC). The proposed methodology is applied and tested on the IEEE nine bus system, providing a comprehensive analysis of the performance of multiple UPFCs.
The inspiration behind this research paper stems from a sense of social responsibility to mitigat... more The inspiration behind this research paper stems from a sense of social responsibility to mitigate road accidents. Its primary objective is to enhance the safety and security of motorcycle riders by promoting helmet usage. The proposed system incorporates a mechanism that prevents the motorcycle from starting unless the rider wears a helmet and is not under the influence of alcohol. Furthermore, the system utilizes sensors to alert the rider of any obstacles approaching from the rear. In the unfortunate event of an accident, the GSM module promptly sends distress signals to the nearest police station, registered relatives, and other designated individuals.
Ensuring high-quality and reliable power supply to customers plays a crucial role in the power sy... more Ensuring high-quality and reliable power supply to customers plays a crucial role in the power system. Restructuring the power system is an efficient approach to evaluate and deliver economic and uninterrupted power supply. Reliability assessment, which depends on system security and adequacy, serves as a means to check the reliability of the power system. Sequential simulation is proposed as a reliable method to enhance power system performance indices and assess reliability in unbundled power utilities. By simulating the stochastic behavior and sequential process of the system, outage occurrences can be identified and reflected in the form of a reliability index. This paper presents an effective method to improve power system reliability in the transmission and distribution network.
This research study delves into the analysis of gender-based patterns in two-wheeler electric veh... more This research study delves into the analysis of gender-based patterns in two-wheeler electric vehicle (EV) riding with respect to accidents. As electric mobility gains momentum worldwide, it becomes essential to understand the potential differences in accident rates, risk factors, and safety practices between male and female riders. The objective of this research is to identify and compare accident trends, driving behaviors, and risk perceptions associated with male and female two-wheeler EV riders, providing valuable insights to enhance safety measures and promote gender-specific accident prevention strategies. The research employs a mixed-method approach, combining the analysis of historical accident data and surveys. Accident data are collected from reliable sources, including traffic authorities, police reports, and insurance records, to uncover patterns in accident occurrence, severity, and contributing factors specific to each gender. Additionally, surveys and questionnaires are distributed to representative samples of male and female electric two-wheeler riders to capture driving behaviors, safety practices, and accident experiences.
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