Pub Date : 2022-10-25DOI: 10.1109/SmartGridComm52983.2022.9960998
Saad Alzahrani, J. Mitra
Microgrid protection continues to be an emerging research problem for several reasons, such as integrating various levels of distributed generation and connecting power electronic converters. This paper develops a new protection approach using Multi-Agent System with a state observer and fault current limiters. This approach has two functions: achieving the fault detection for multiple zones of microgrid and restoring the power to the affected load in case of persistent fault. Mainly, this integration framework comprises distributed agents, which will communicate, interact, and exchange data for detecting the fault through the residual current value of the state observer within a particular protection zone. On the other hand, the fault current limiter will prevent the interruption of distributed generators during the faults. The proposed protection framework in this paper has been tested and applied to a microgrid configuration and is demonstrated to be an effective means to detect the faults as well as restore the power for multiple protection zones of the system.
{"title":"Microgrid Fault Detection Utilizing State Observer and Multi-Agent System","authors":"Saad Alzahrani, J. Mitra","doi":"10.1109/SmartGridComm52983.2022.9960998","DOIUrl":"https://doi.org/10.1109/SmartGridComm52983.2022.9960998","url":null,"abstract":"Microgrid protection continues to be an emerging research problem for several reasons, such as integrating various levels of distributed generation and connecting power electronic converters. This paper develops a new protection approach using Multi-Agent System with a state observer and fault current limiters. This approach has two functions: achieving the fault detection for multiple zones of microgrid and restoring the power to the affected load in case of persistent fault. Mainly, this integration framework comprises distributed agents, which will communicate, interact, and exchange data for detecting the fault through the residual current value of the state observer within a particular protection zone. On the other hand, the fault current limiter will prevent the interruption of distributed generators during the faults. The proposed protection framework in this paper has been tested and applied to a microgrid configuration and is demonstrated to be an effective means to detect the faults as well as restore the power for multiple protection zones of the system.","PeriodicalId":252202,"journal":{"name":"2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130580054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-25DOI: 10.1109/SmartGridComm52983.2022.9961027
Anchal Ahalawat, Sridhar Adepu, Joseph Gardiner
The electric vehicle (EV) charging system plays a significant role in the future of energy systems. The widespread adoption of operating EV charging is accelerates the integration of transmission and distribution systems, this helps to accommodate a clean atmosphere and drop conventional fuel dependence. An EV interacts with different objects while recharging in the charging station. The charging stations that power up such vehicles can also be connected to the internet and make them particularly to malicious attack through hacking or remote accessing. Thus, this technology has caught the attention of many researchers who have proposed authentication protocols to provide a secure connection for exchanging information between electric vehicles and the charging station. This article discusses comprehensive security threats in the EV charging systems. Moreover, it reviews the architecture of the charging station system and the protocols between electric vehicles and charging stations.
{"title":"Security Threats in Electric Vehicle Charging","authors":"Anchal Ahalawat, Sridhar Adepu, Joseph Gardiner","doi":"10.1109/SmartGridComm52983.2022.9961027","DOIUrl":"https://doi.org/10.1109/SmartGridComm52983.2022.9961027","url":null,"abstract":"The electric vehicle (EV) charging system plays a significant role in the future of energy systems. The widespread adoption of operating EV charging is accelerates the integration of transmission and distribution systems, this helps to accommodate a clean atmosphere and drop conventional fuel dependence. An EV interacts with different objects while recharging in the charging station. The charging stations that power up such vehicles can also be connected to the internet and make them particularly to malicious attack through hacking or remote accessing. Thus, this technology has caught the attention of many researchers who have proposed authentication protocols to provide a secure connection for exchanging information between electric vehicles and the charging station. This article discusses comprehensive security threats in the EV charging systems. Moreover, it reviews the architecture of the charging station system and the protocols between electric vehicles and charging stations.","PeriodicalId":252202,"journal":{"name":"2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131938656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-25DOI: 10.1109/SmartGridComm52983.2022.9961055
Tobias Brudermueller, F. Wirth, Andreas Weigert, T. Staake
With the increasing prevalence of heat pumps in private households, the need for optimization is growing. At the same time, the growing number of active smart electricity meters generates data that can be used for remote monitoring. In this paper, we focus on the automatic differentiation between fixed speed and variable speed heat pumps using smart meter data. This distinction is relevant because it is necessary for evaluating the state or cyclic behavior of a heat pump. In addition, identifying fixed speed heat pumps is important because they are known to be the less efficient systems and therefore may be preferred targets in energy efficiency or replacement campaigns. Our methods are applied to electricity data from 171 Swiss households with a resolution of 15 minutes. In this setting, a K-Nearest Neighbor model achieves a mean AUC of 0.976 compared to 0.5 of a biased random guess model.
{"title":"Automatic Differentiation of Variable and Fixed Speed Heat Pumps With Smart Meter Data","authors":"Tobias Brudermueller, F. Wirth, Andreas Weigert, T. Staake","doi":"10.1109/SmartGridComm52983.2022.9961055","DOIUrl":"https://doi.org/10.1109/SmartGridComm52983.2022.9961055","url":null,"abstract":"With the increasing prevalence of heat pumps in private households, the need for optimization is growing. At the same time, the growing number of active smart electricity meters generates data that can be used for remote monitoring. In this paper, we focus on the automatic differentiation between fixed speed and variable speed heat pumps using smart meter data. This distinction is relevant because it is necessary for evaluating the state or cyclic behavior of a heat pump. In addition, identifying fixed speed heat pumps is important because they are known to be the less efficient systems and therefore may be preferred targets in energy efficiency or replacement campaigns. Our methods are applied to electricity data from 171 Swiss households with a resolution of 15 minutes. In this setting, a K-Nearest Neighbor model achieves a mean AUC of 0.976 compared to 0.5 of a biased random guess model.","PeriodicalId":252202,"journal":{"name":"2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130755071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-25DOI: 10.1109/SmartGridComm52983.2022.9961024
Kang Pu, Yue Zhao
With the rapidly evolving penetration of electric vehicles (EVs) in power distribution systems, a major issue that utilities face is the lack of visibility into the charging behaviors of the behind-the-meter (BTM) EVs. Knowing the BTM EV charging behaviors can greatly enhance utilities' system planning and operation efficacy. In this paper, the problem of disaggregating BTM EV load traces from smart meter data traces is studied. Based on the characteristics of typical EV charging traces, three interdependent sub-problems are formulated: a) Detecting the presence of BTM EVs, b) Estimating the EV charging rate, and c) Detecting the EV charging periods. A unified iterative algorithmic framework is developed to solve all three sub-problems. Importantly, the proposed algorithms do not assume or utilize the knowledge of ground truth EV load traces but estimate BTM EV load traces in an “unsupervised” fashion. Numerical evaluation is conducted based on real-world 15-minute interval smart meter data from Austin, TX, and demonstrates great performance achieved by the proposed algorithms.
{"title":"Behind-the-Meter Disaggregation of Residential Electric Vehicle Charging Load","authors":"Kang Pu, Yue Zhao","doi":"10.1109/SmartGridComm52983.2022.9961024","DOIUrl":"https://doi.org/10.1109/SmartGridComm52983.2022.9961024","url":null,"abstract":"With the rapidly evolving penetration of electric vehicles (EVs) in power distribution systems, a major issue that utilities face is the lack of visibility into the charging behaviors of the behind-the-meter (BTM) EVs. Knowing the BTM EV charging behaviors can greatly enhance utilities' system planning and operation efficacy. In this paper, the problem of disaggregating BTM EV load traces from smart meter data traces is studied. Based on the characteristics of typical EV charging traces, three interdependent sub-problems are formulated: a) Detecting the presence of BTM EVs, b) Estimating the EV charging rate, and c) Detecting the EV charging periods. A unified iterative algorithmic framework is developed to solve all three sub-problems. Importantly, the proposed algorithms do not assume or utilize the knowledge of ground truth EV load traces but estimate BTM EV load traces in an “unsupervised” fashion. Numerical evaluation is conducted based on real-world 15-minute interval smart meter data from Austin, TX, and demonstrates great performance achieved by the proposed algorithms.","PeriodicalId":252202,"journal":{"name":"2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121161080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-25DOI: 10.1109/SmartGridComm52983.2022.9960997
Wenyu Wang, N. Yu, Yue Zhao
In distribution systems with growing distributed energy resources, accurate estimation of network parameters is crucial to feeder modeling, monitoring and management. Al-though existing state-of-the-art parameter estimation algorithms such as physics-informed graphical learning (GL) have accurate estimation, they can potentially suffer from scalability issues due to slow training in larger networks. In this paper, we propose an upgraded graphical learning method called fast graphical learning (FGL) to improve the computational efficiency and scalability while preserving the merits of GL. In FGL, we develop faster alternative algorithms to replace the fixed-point-iteration-based FORWARD and BACKWARD algorithms in GL. These alternative algorithms are based on fast power flow calculation of the current injection method and more efficient state initialization by the linearized power flow model. A comprehensive numerical study on IEEE test feeders and large-scale real-world distribution feeders shows that FGL improves the computational efficiency by as much as 60 times in larger distribution networks while attaining the accuracy of the state-of-art algorithms.
{"title":"Fast Graphical Learning Method for Parameter Estimation in Large-Scale Distribution Networks","authors":"Wenyu Wang, N. Yu, Yue Zhao","doi":"10.1109/SmartGridComm52983.2022.9960997","DOIUrl":"https://doi.org/10.1109/SmartGridComm52983.2022.9960997","url":null,"abstract":"In distribution systems with growing distributed energy resources, accurate estimation of network parameters is crucial to feeder modeling, monitoring and management. Al-though existing state-of-the-art parameter estimation algorithms such as physics-informed graphical learning (GL) have accurate estimation, they can potentially suffer from scalability issues due to slow training in larger networks. In this paper, we propose an upgraded graphical learning method called fast graphical learning (FGL) to improve the computational efficiency and scalability while preserving the merits of GL. In FGL, we develop faster alternative algorithms to replace the fixed-point-iteration-based FORWARD and BACKWARD algorithms in GL. These alternative algorithms are based on fast power flow calculation of the current injection method and more efficient state initialization by the linearized power flow model. A comprehensive numerical study on IEEE test feeders and large-scale real-world distribution feeders shows that FGL improves the computational efficiency by as much as 60 times in larger distribution networks while attaining the accuracy of the state-of-art algorithms.","PeriodicalId":252202,"journal":{"name":"2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127720132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-25DOI: 10.1109/SmartGridComm52983.2022.9961042
Ghada Elbez, K. Nahrstedt, V. Hagenmeyer
The availability of communication in IEC 61850 substations can be hindered by Denial of Service (DoS) that result from an advanced Generic Object Oriented Substation Event (GOOSE) poisoning attacks. To the best of our knowledge, most of the available approaches in the literature are unable to detect similar attacks and none of them can offer the detection in an early manner. Thus, we develop the Early Detection of Attacks for GOOSE Network Traffic (EDA4GNeT) method that takes into account the specific features of IEC 61850 substations and offers a good trade-off between detection performance and detection time. To validate the efficiency of the novel anomaly detection method against those specific GOOSE poisoning attacks, a comparison with the closest works to ours is conducted in a similar use case representing a T1-1 substation. Results demonstrate the possibility of an early detection approximately 37 time samples ahead and an average detection rate of EDA4GNeT of more than 99 % with a low false positive rate of less than 1 %.
{"title":"Early Detection of GOOSE Denial of Service (DoS) Attacks in IEC 61850 Substations","authors":"Ghada Elbez, K. Nahrstedt, V. Hagenmeyer","doi":"10.1109/SmartGridComm52983.2022.9961042","DOIUrl":"https://doi.org/10.1109/SmartGridComm52983.2022.9961042","url":null,"abstract":"The availability of communication in IEC 61850 substations can be hindered by Denial of Service (DoS) that result from an advanced Generic Object Oriented Substation Event (GOOSE) poisoning attacks. To the best of our knowledge, most of the available approaches in the literature are unable to detect similar attacks and none of them can offer the detection in an early manner. Thus, we develop the Early Detection of Attacks for GOOSE Network Traffic (EDA4GNeT) method that takes into account the specific features of IEC 61850 substations and offers a good trade-off between detection performance and detection time. To validate the efficiency of the novel anomaly detection method against those specific GOOSE poisoning attacks, a comparison with the closest works to ours is conducted in a similar use case representing a T1-1 substation. Results demonstrate the possibility of an early detection approximately 37 time samples ahead and an average detection rate of EDA4GNeT of more than 99 % with a low false positive rate of less than 1 %.","PeriodicalId":252202,"journal":{"name":"2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121335714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-25DOI: 10.1109/SmartGridComm52983.2022.9960983
George Torres, Sharad Shrestha, S. Misra
With the proliferation of differently-abled and het-erogeneous devices in the smart grid Denial of Service (DoS) is becoming an even more potent attack vector than it was before. This paper demonstrates the ease with which an adversary can orchestrate DoS and distributed DoS (DDoS) attacks on the grid. We then propose iCAD-an information-centric ar-chitecture, which extends the iCAAP architecture proposed by us [8], complete with mitigation strategies built for DoS/DDoS resilience. We discuss our architecture in detail and demonstrate the architecture and the mitigation technique's effectiveness in mitigating DoS/DDoS attacks in the face of significant attack load from the distributed agents.
{"title":"iCAD: information-Centric network Architecture for DDoS Protection in the Smart Grid","authors":"George Torres, Sharad Shrestha, S. Misra","doi":"10.1109/SmartGridComm52983.2022.9960983","DOIUrl":"https://doi.org/10.1109/SmartGridComm52983.2022.9960983","url":null,"abstract":"With the proliferation of differently-abled and het-erogeneous devices in the smart grid Denial of Service (DoS) is becoming an even more potent attack vector than it was before. This paper demonstrates the ease with which an adversary can orchestrate DoS and distributed DoS (DDoS) attacks on the grid. We then propose iCAD-an information-centric ar-chitecture, which extends the iCAAP architecture proposed by us [8], complete with mitigation strategies built for DoS/DDoS resilience. We discuss our architecture in detail and demonstrate the architecture and the mitigation technique's effectiveness in mitigating DoS/DDoS attacks in the face of significant attack load from the distributed agents.","PeriodicalId":252202,"journal":{"name":"2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126376536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-25DOI: 10.1109/SmartGridComm52983.2022.9961020
Jie Liu, Shuoyao Wang, Xiaoying Tang
The rapid adoption of electric vehicles (EVs) stimulates the proliferation of charging stations (CSs), motivating the cooperative management of growing CSs. However, cooperative CS management still remains an open problem, due to the uncertain user behavior and heterogeneous service capabilities. To capture the CS dynamics caused by uncertain user behavior, we propose a deep reinforcement learning (DRL)-based cooperative method for multiple CSs, towards maximizing the total profit. The proposed method determines pricing and charging scheduling decisions for CSs, considering stochastic CSs selection and its impact on CSs energy supply. In order to reduce the computational burden of dimensions caused by the time-varying decisions, we design a discretization strategy for action space, based on the current market rule of tiered pricing and CS types. The simulations using real data demonstrate that our proposed method can obtain higher profit than the independent operation and benchmark cooperative algorithms such as Q-learning.
{"title":"Pricing and Charging Scheduling for Cooperative Electric Vehicle Charging Stations via Deep Reinforcement Learning","authors":"Jie Liu, Shuoyao Wang, Xiaoying Tang","doi":"10.1109/SmartGridComm52983.2022.9961020","DOIUrl":"https://doi.org/10.1109/SmartGridComm52983.2022.9961020","url":null,"abstract":"The rapid adoption of electric vehicles (EVs) stimulates the proliferation of charging stations (CSs), motivating the cooperative management of growing CSs. However, cooperative CS management still remains an open problem, due to the uncertain user behavior and heterogeneous service capabilities. To capture the CS dynamics caused by uncertain user behavior, we propose a deep reinforcement learning (DRL)-based cooperative method for multiple CSs, towards maximizing the total profit. The proposed method determines pricing and charging scheduling decisions for CSs, considering stochastic CSs selection and its impact on CSs energy supply. In order to reduce the computational burden of dimensions caused by the time-varying decisions, we design a discretization strategy for action space, based on the current market rule of tiered pricing and CS types. The simulations using real data demonstrate that our proposed method can obtain higher profit than the independent operation and benchmark cooperative algorithms such as Q-learning.","PeriodicalId":252202,"journal":{"name":"2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131077816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-25DOI: 10.1109/SmartGridComm52983.2022.9961000
S. Köhler, S. Birnbach, Richard Baker, I. Martinovic
The adoption of fully Electric Vehicles (EVs) is happening at a rapid pace. To make the charging as fast and convenient as possible, new charging approaches are developed constantly. One such approach is wireless charging, also known as Wireless Power Transfer (WPT). Instead of charging an EV via a charging cable, the battery is charged wirelessly. For safety and efficiency reasons, the vehicle and the charging station continuously exchange critical information about the charging process. This includes, e.g., the maximum voltage and current, battery temperature, and State of Charge (SoC). Since there is no physical connection between the vehicle and the charging station, this necessary control communication has to be implemented as a wireless connection. However, if the communication is interrupted, the charging process is aborted for safety reasons. In this paper, we analyze the attack surface of EV charging standards that use such a wireless control communication. More specifically, we discuss potential wireless attacks that can violate the availability and analyze the implemented security features of a real-world wireless charging station that has already been deployed. We found that the tested charging station does not implement even simple security measures, such as IEEE 802.11w, that can protect the communication from denial-of-service attacks. Finally, we discuss potential countermeasures, and give recommendations to improve the security and increase the resilience of wireless charging.
{"title":"On the Security of the Wireless Electric Vehicle Charging Communication","authors":"S. Köhler, S. Birnbach, Richard Baker, I. Martinovic","doi":"10.1109/SmartGridComm52983.2022.9961000","DOIUrl":"https://doi.org/10.1109/SmartGridComm52983.2022.9961000","url":null,"abstract":"The adoption of fully Electric Vehicles (EVs) is happening at a rapid pace. To make the charging as fast and convenient as possible, new charging approaches are developed constantly. One such approach is wireless charging, also known as Wireless Power Transfer (WPT). Instead of charging an EV via a charging cable, the battery is charged wirelessly. For safety and efficiency reasons, the vehicle and the charging station continuously exchange critical information about the charging process. This includes, e.g., the maximum voltage and current, battery temperature, and State of Charge (SoC). Since there is no physical connection between the vehicle and the charging station, this necessary control communication has to be implemented as a wireless connection. However, if the communication is interrupted, the charging process is aborted for safety reasons. In this paper, we analyze the attack surface of EV charging standards that use such a wireless control communication. More specifically, we discuss potential wireless attacks that can violate the availability and analyze the implemented security features of a real-world wireless charging station that has already been deployed. We found that the tested charging station does not implement even simple security measures, such as IEEE 802.11w, that can protect the communication from denial-of-service attacks. Finally, we discuss potential countermeasures, and give recommendations to improve the security and increase the resilience of wireless charging.","PeriodicalId":252202,"journal":{"name":"2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133208423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-25DOI: 10.1109/SmartGridComm52983.2022.9961019
Md. Navid Bin Anwar, Rukhsana Ruby, Yijun Cheng, Jianping Pan
Electric vehicle charging stations (EVCS) play a vital role in providing charging support to EV users. In order to facilitate users in terms of charging speed, two different charging modes (L2 and L3) are currently available at public charging stations. L3 mode provides quick charging with higher power, whereas L2 mode offers moderate charging speed with low power. The integration of an EVCS into the power grid requires coordinated charging strategies in order to reduce the electricity bill for a profitable operation. However, the effective utilization of the multi-mode charging strategy to serve the maximum number of EVs for a small charging station with limited charging capacity and spots is an open issue. To this end, we propose a priority-based online charging scheme, namely PBOS, which is based on real-time information and does not depend on future knowledge. The objective is to serve as many vehicles as possible in a day while fulfilling their charging requirements under a multi-mode EVCS setting and reducing the charging costs by utilizing the time-of-use pricing based demand response strategy. Simulation results show that the proposed algorithm can increase profit for EVCS by up to 42% with a 20% lower rejection rate when compared with other schemes.
{"title":"Time-of-Use-Aware Priority-Based Multi-Mode Online Charging Scheme for EV Charging Stations","authors":"Md. Navid Bin Anwar, Rukhsana Ruby, Yijun Cheng, Jianping Pan","doi":"10.1109/SmartGridComm52983.2022.9961019","DOIUrl":"https://doi.org/10.1109/SmartGridComm52983.2022.9961019","url":null,"abstract":"Electric vehicle charging stations (EVCS) play a vital role in providing charging support to EV users. In order to facilitate users in terms of charging speed, two different charging modes (L2 and L3) are currently available at public charging stations. L3 mode provides quick charging with higher power, whereas L2 mode offers moderate charging speed with low power. The integration of an EVCS into the power grid requires coordinated charging strategies in order to reduce the electricity bill for a profitable operation. However, the effective utilization of the multi-mode charging strategy to serve the maximum number of EVs for a small charging station with limited charging capacity and spots is an open issue. To this end, we propose a priority-based online charging scheme, namely PBOS, which is based on real-time information and does not depend on future knowledge. The objective is to serve as many vehicles as possible in a day while fulfilling their charging requirements under a multi-mode EVCS setting and reducing the charging costs by utilizing the time-of-use pricing based demand response strategy. Simulation results show that the proposed algorithm can increase profit for EVCS by up to 42% with a 20% lower rejection rate when compared with other schemes.","PeriodicalId":252202,"journal":{"name":"2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130111870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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