In this paper, we consider the resource allocation of mission-critical services in the smart grid, where we deploy an intelligent reflecting surface (IRS) during the transmission to alleviate the shortage of cooperative non-orthogonal multiple access (C-NOMA) in ultra-reliable and low-latency communications (URLLC). The purpose of this paper is to jointly optimize the power allocation, IRS phase shift, and the blocklength with finite blocklength information theory to minimize the total energy consumption subject to their delay and reliability constraints. Since the formulated optimization is non-convex, we first introduced two lemmas to simplify the constraints, and then we solve the optimization problem via the alternating optimization (AO). The transmit power and the blocklengths are optimized by using the techniques of successive convex approximation (SCA) and arithmetic geometry mean (AGM), and the reflective beamforming is optimized by using the techniques of semi-define relaxation (SDR) and sequential rank-one constraint relaxation (SROCR). Simulation results validate the advantage of IRS to C-NOMA in URLLC and the effectiveness of the resource allocation.
{"title":"Resource Allocation for Intelligent Reflecting Surface-Assisted Cooperative NOMA-URLLC Networks in Smart Grid","authors":"Junjie Yang, Geng Liu, J. Ren, Ying Liu, Liang Yao, Yuchen Zhou, Jian Chen","doi":"10.1109/SmartGridComm52983.2022.9961057","DOIUrl":"https://doi.org/10.1109/SmartGridComm52983.2022.9961057","url":null,"abstract":"In this paper, we consider the resource allocation of mission-critical services in the smart grid, where we deploy an intelligent reflecting surface (IRS) during the transmission to alleviate the shortage of cooperative non-orthogonal multiple access (C-NOMA) in ultra-reliable and low-latency communications (URLLC). The purpose of this paper is to jointly optimize the power allocation, IRS phase shift, and the blocklength with finite blocklength information theory to minimize the total energy consumption subject to their delay and reliability constraints. Since the formulated optimization is non-convex, we first introduced two lemmas to simplify the constraints, and then we solve the optimization problem via the alternating optimization (AO). The transmit power and the blocklengths are optimized by using the techniques of successive convex approximation (SCA) and arithmetic geometry mean (AGM), and the reflective beamforming is optimized by using the techniques of semi-define relaxation (SDR) and sequential rank-one constraint relaxation (SROCR). Simulation results validate the advantage of IRS to C-NOMA in URLLC and the effectiveness of the resource allocation.","PeriodicalId":252202,"journal":{"name":"2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"40 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":"126828409","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/SmartGridComm52983.2022.9960979
P. Raussi, J. Kilpi, H. Kokkoniemi-Tarkkanen, A. Kulmala, P. Hovila
The distribution of smart grid applications to different physical devices not interconnected with physical sensors has opened the possibility for software virtualization allowing flexible localization of functionalities. Harnessing wireless 5G technology enables edge computing and locating smart grid applications at the edge. In this paper, we study edge computing supporting medium voltage grid fault location, discuss the challenges and benefits of bringing smart grid applications to the edge, and demonstrate fault location operation on an edge device. The challenges and benefits undertaken for a good business case are highlighted. The demonstration shows that the total data rate in urban areas is the critical parameter, whereas latency due to large distances and the general availability of edge resources are the most significant issues in rural areas.
{"title":"Edge Computing supported Fault Indication in Smart Grid","authors":"P. Raussi, J. Kilpi, H. Kokkoniemi-Tarkkanen, A. Kulmala, P. Hovila","doi":"10.1109/SmartGridComm52983.2022.9960979","DOIUrl":"https://doi.org/10.1109/SmartGridComm52983.2022.9960979","url":null,"abstract":"The distribution of smart grid applications to different physical devices not interconnected with physical sensors has opened the possibility for software virtualization allowing flexible localization of functionalities. Harnessing wireless 5G technology enables edge computing and locating smart grid applications at the edge. In this paper, we study edge computing supporting medium voltage grid fault location, discuss the challenges and benefits of bringing smart grid applications to the edge, and demonstrate fault location operation on an edge device. The challenges and benefits undertaken for a good business case are highlighted. The demonstration shows that the total data rate in urban areas is the critical parameter, whereas latency due to large distances and the general availability of edge resources are the most significant issues in rural areas.","PeriodicalId":252202,"journal":{"name":"2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"55 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":"128711548","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/SmartGridComm52983.2022.9961016
Ömer Sen, C. Eze, Andreas Ulbig, A. Monti
While digitization of distribution grids through information and communications technology brings numerous benefits, it also increases the grid's vulnerability to serious cyber attacks. Unlike conventional systems, attacks on many industrial control systems such as power grids often occur in multiple stages, with the attacker taking several steps at once to achieve its goal. Detection mechanisms with situational awareness are needed to detect orchestrated attack steps as part of a coherent attack campaign. To provide a foundation for detection and prevention of such attacks, this paper addresses the detection of multi-stage cyber attacks with the aid of a graph-based cyber intelligence database and alert correlation approach. Specifically, we propose an approach to detect multi-stage attacks by lever-aging heterogeneous data to form a knowledge base and employ a model-based correlation approach on the generated alerts to identify multi-stage cyber attack sequences taking place in the network. We investigate the detection quality of the proposed approach by using a case study of a multi-stage cyber attack campaign in a future-orientated power grid pilot.
{"title":"On Holistic Multi-Step Cyberattack Detection via a Graph-based Correlation Approach","authors":"Ömer Sen, C. Eze, Andreas Ulbig, A. Monti","doi":"10.1109/SmartGridComm52983.2022.9961016","DOIUrl":"https://doi.org/10.1109/SmartGridComm52983.2022.9961016","url":null,"abstract":"While digitization of distribution grids through information and communications technology brings numerous benefits, it also increases the grid's vulnerability to serious cyber attacks. Unlike conventional systems, attacks on many industrial control systems such as power grids often occur in multiple stages, with the attacker taking several steps at once to achieve its goal. Detection mechanisms with situational awareness are needed to detect orchestrated attack steps as part of a coherent attack campaign. To provide a foundation for detection and prevention of such attacks, this paper addresses the detection of multi-stage cyber attacks with the aid of a graph-based cyber intelligence database and alert correlation approach. Specifically, we propose an approach to detect multi-stage attacks by lever-aging heterogeneous data to form a knowledge base and employ a model-based correlation approach on the generated alerts to identify multi-stage cyber attack sequences taking place in the network. We investigate the detection quality of the proposed approach by using a case study of a multi-stage cyber attack campaign in a future-orientated power grid pilot.","PeriodicalId":252202,"journal":{"name":"2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"10 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":"131412775","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/SmartGridComm52983.2022.9961032
Leo Strobel, M. Pruckner
Car sharing is a more sustainable approach to personal mobility than vehicle ownership, especially if car sharing services electrify their fleets. However, due to the limited range and slow charging process of electric vehicles, car sharing providers might face problems keeping the vehicles adequately charged. This paper analyzes 4.5 years of real booking data from a German two-way car sharing provider. The dataset includes information on the booking time window, driven distance, and location. We use this data to study the customer behavior and simulate the past operation with a completely electrified fleet. Based on the simulation, we determine whether charging problems exist and how they can be solved by adapting the charging rate, battery capacity, and number of charging points. Our results indicate that the operation of the service with modern electric vehicles is entirely feasible. Per car sharing station, two charging points are sufficient if vehicles do not have to be connected immediately upon arrival (by the customer), but can connect later once another vehicle has finished charging. Somewhat problematic is that 4% of the bookings have a longer driven distance than the vehicle's range. In these cases, the disutility to customers is unclear but, in all likelihood, manageable. Furthermore, we find that 50% of the charging events can be shifted by more than 10h, indicating significant flexibility that could be utilized for smart charging and the provision of ancillary services in the future.
{"title":"Feasibility of completely electrified two-way car sharing","authors":"Leo Strobel, M. Pruckner","doi":"10.1109/SmartGridComm52983.2022.9961032","DOIUrl":"https://doi.org/10.1109/SmartGridComm52983.2022.9961032","url":null,"abstract":"Car sharing is a more sustainable approach to personal mobility than vehicle ownership, especially if car sharing services electrify their fleets. However, due to the limited range and slow charging process of electric vehicles, car sharing providers might face problems keeping the vehicles adequately charged. This paper analyzes 4.5 years of real booking data from a German two-way car sharing provider. The dataset includes information on the booking time window, driven distance, and location. We use this data to study the customer behavior and simulate the past operation with a completely electrified fleet. Based on the simulation, we determine whether charging problems exist and how they can be solved by adapting the charging rate, battery capacity, and number of charging points. Our results indicate that the operation of the service with modern electric vehicles is entirely feasible. Per car sharing station, two charging points are sufficient if vehicles do not have to be connected immediately upon arrival (by the customer), but can connect later once another vehicle has finished charging. Somewhat problematic is that 4% of the bookings have a longer driven distance than the vehicle's range. In these cases, the disutility to customers is unclear but, in all likelihood, manageable. Furthermore, we find that 50% of the charging events can be shifted by more than 10h, indicating significant flexibility that could be utilized for smart charging and the provision of ancillary services in the future.","PeriodicalId":252202,"journal":{"name":"2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"87 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":"127455675","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/SmartGridComm52983.2022.9960976
Huamin Ren, Xiaomeng Su, R. Jenssen, Jingyue Li, S. Anfinsen
With the prevalence of smart meter infrastructure, data analysis on consumer side becomes more and more important in smart grid systems. One of the fundamental tasks is to disaggregate users' total consumption into appliance-wise values. It has been well noted that encoding of temporal dependency is a key issue for successful modelling of the relations between the total consumption and its decomposed consumption on an appliance historically, and therefore has been implemented in many state-of-the-art models. However, how to encode the varied long-term and short-term dependency coming from different appliances is yet an open and under-addressed question. In this paper, we propose an attention-guided temporal convolutional network (ATCN), which generates different temporal residual blocks and provides an attention mechanism to indicate the importance of those blocks with respect to the appliance. Ul-timately, we aim to address these two questions: i) How to employ both long-term and short-term temporal dependency to better disaggregate future loads while maintaining an affordable memory cost? ii) How to employ attention during the training of an appliance to obtain a better representation of the consumption pattern? We have demonstrated the effectiveness of our approach through comprehensive experiments and show that our proposed ATCN model achieves state-of-the-art performance, particularly on multi-status appliances that are normally hard to cope with regarding disaggregation accuracy and generalization capability.
{"title":"Attention-guided Temporal Convolutional Network for Non-intrusive Load Monitoring","authors":"Huamin Ren, Xiaomeng Su, R. Jenssen, Jingyue Li, S. Anfinsen","doi":"10.1109/SmartGridComm52983.2022.9960976","DOIUrl":"https://doi.org/10.1109/SmartGridComm52983.2022.9960976","url":null,"abstract":"With the prevalence of smart meter infrastructure, data analysis on consumer side becomes more and more important in smart grid systems. One of the fundamental tasks is to disaggregate users' total consumption into appliance-wise values. It has been well noted that encoding of temporal dependency is a key issue for successful modelling of the relations between the total consumption and its decomposed consumption on an appliance historically, and therefore has been implemented in many state-of-the-art models. However, how to encode the varied long-term and short-term dependency coming from different appliances is yet an open and under-addressed question. In this paper, we propose an attention-guided temporal convolutional network (ATCN), which generates different temporal residual blocks and provides an attention mechanism to indicate the importance of those blocks with respect to the appliance. Ul-timately, we aim to address these two questions: i) How to employ both long-term and short-term temporal dependency to better disaggregate future loads while maintaining an affordable memory cost? ii) How to employ attention during the training of an appliance to obtain a better representation of the consumption pattern? We have demonstrated the effectiveness of our approach through comprehensive experiments and show that our proposed ATCN model achieves state-of-the-art performance, particularly on multi-status appliances that are normally hard to cope with regarding disaggregation accuracy and generalization capability.","PeriodicalId":252202,"journal":{"name":"2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"278 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":"126219256","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/SmartGridComm52983.2022.9961059
Olamide Jogunola, B. Adebisi, H. Gačanin, M. Hammoudeh, Guan Gui
As peer-to-peer energy trading and local energy market are gaining momentum, a follow-up challenge is scaling up to include multi-community, multi-region power schedule and trading. This study introduces community-to-community power trading and schedules considering various generating units, including coal, gas, wind, and solar, as well as import and export prices from community transactions. These generating sources are used to fulfil the demand requirements of each community over a time horizon, as well as absorbing or trading the margin among the inter-connected communities, while fulfilling certain distribution network constraints. For a practical case, the uncertainties in wind power generations are considered. An optimality condition decomposition technique is used to decompose the overall problem into a community-based local problem. Thus, individual community solves their optimisation local problem in parallel for operational efficiency of real-time multi-commodity power schedule and trading. The initial results indicate that each community acts in its best interest to minimise its costs when there is a change in the variable. When emission costs are applied as a constraint to their generation, a reduction in power generation is observed augmented by an increase of up to 30.8% of power transferred in the inter-community transaction.
{"title":"Optimal Dynamic Multi-source Multi-community Power Schedule and Trading","authors":"Olamide Jogunola, B. Adebisi, H. Gačanin, M. Hammoudeh, Guan Gui","doi":"10.1109/SmartGridComm52983.2022.9961059","DOIUrl":"https://doi.org/10.1109/SmartGridComm52983.2022.9961059","url":null,"abstract":"As peer-to-peer energy trading and local energy market are gaining momentum, a follow-up challenge is scaling up to include multi-community, multi-region power schedule and trading. This study introduces community-to-community power trading and schedules considering various generating units, including coal, gas, wind, and solar, as well as import and export prices from community transactions. These generating sources are used to fulfil the demand requirements of each community over a time horizon, as well as absorbing or trading the margin among the inter-connected communities, while fulfilling certain distribution network constraints. For a practical case, the uncertainties in wind power generations are considered. An optimality condition decomposition technique is used to decompose the overall problem into a community-based local problem. Thus, individual community solves their optimisation local problem in parallel for operational efficiency of real-time multi-commodity power schedule and trading. The initial results indicate that each community acts in its best interest to minimise its costs when there is a change in the variable. When emission costs are applied as a constraint to their generation, a reduction in power generation is observed augmented by an increase of up to 30.8% of power transferred in the inter-community transaction.","PeriodicalId":252202,"journal":{"name":"2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"26 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":"121650623","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/SmartGridComm52983.2022.9960996
A. Umunnakwe, Patrick Wlazlo, A. Sahu, Julian Velasquez, K. Davis, A. Goulart, S. Zonouz
The daily operations of critical infrastructures have long relied upon computer networks. Nevertheless, these networks attract adversarial actions. To improve the security and resilience of electric power systems and other cyber-physical critical infrastructure, there is a crucial need to study their communication networks alongside their physical systems. However, there is a disconnect between network models used by research groups and the actual network topologies used in industry. These modeling differences lead to discrepancies between study results and what is attainable in the field. To address this, OpenConduit is introduced in this paper. OpenConduit is designed to achieve automated and realistic replication of electric power system networks in an emulation environment. OpenConduit interprets industrial networks' configuration data (real or synthetic) and rebuilds the network in the Common Open Research Emulator (CORE). OpenConduit's architecture, design, and integration into a large-scale cyber-physical testbed are the focus of the paper. Experiments with a sample synthetic electric utility network show its ability to efficiently enable detailed emulation studies for real utility networks in a safe environment. Finally, experiments on a range of cases demonstrate the OpenConduit tool to be effective for scalability in the emulation of larger networks, as well as achieving conformity with configuration files and system settings while maintaining functionality. Additionally, the emulation time which averages 59 seconds can be integrated with power systems operations, while upholding information security of system data.
关键基础设施的日常运作长期依赖于计算机网络。然而，这些网络吸引了敌对行动。为了提高电力系统和其他网络物理关键基础设施的安全性和弹性，迫切需要在研究其物理系统的同时研究其通信网络。然而，研究小组使用的网络模型与工业中使用的实际网络拓扑之间存在脱节。这些建模上的差异导致了研究结果与该领域实际情况之间的差异。为了解决这个问题，本文介绍了OpenConduit。OpenConduit设计用于在仿真环境中实现电力系统网络的自动化和逼真复制。OpenConduit解释工业网络的配置数据(真实的或合成的)，并在Common Open Research Emulator (CORE)中重建网络。OpenConduit的架构、设计以及与大型网络物理测试平台的集成是本文的重点。仿真实验表明，该方法能够有效地对安全环境下的实际电网进行详细的仿真研究。最后，在一系列案例上的实验表明，OpenConduit工具对于模拟大型网络中的可伸缩性是有效的，并且在保持功能的同时实现与配置文件和系统设置的一致性。此外，仿真时间平均为59秒，可以与电力系统运行相结合，同时保证系统数据的信息安全。
{"title":"OpenConduit: A Tool for Recreating Power System Communication Networks Automatically","authors":"A. Umunnakwe, Patrick Wlazlo, A. Sahu, Julian Velasquez, K. Davis, A. Goulart, S. Zonouz","doi":"10.1109/SmartGridComm52983.2022.9960996","DOIUrl":"https://doi.org/10.1109/SmartGridComm52983.2022.9960996","url":null,"abstract":"The daily operations of critical infrastructures have long relied upon computer networks. Nevertheless, these networks attract adversarial actions. To improve the security and resilience of electric power systems and other cyber-physical critical infrastructure, there is a crucial need to study their communication networks alongside their physical systems. However, there is a disconnect between network models used by research groups and the actual network topologies used in industry. These modeling differences lead to discrepancies between study results and what is attainable in the field. To address this, OpenConduit is introduced in this paper. OpenConduit is designed to achieve automated and realistic replication of electric power system networks in an emulation environment. OpenConduit interprets industrial networks' configuration data (real or synthetic) and rebuilds the network in the Common Open Research Emulator (CORE). OpenConduit's architecture, design, and integration into a large-scale cyber-physical testbed are the focus of the paper. Experiments with a sample synthetic electric utility network show its ability to efficiently enable detailed emulation studies for real utility networks in a safe environment. Finally, experiments on a range of cases demonstrate the OpenConduit tool to be effective for scalability in the emulation of larger networks, as well as achieving conformity with configuration files and system settings while maintaining functionality. Additionally, the emulation time which averages 59 seconds can be integrated with power systems operations, while upholding information security of system data.","PeriodicalId":252202,"journal":{"name":"2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"19 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":"125297406","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/SmartGridComm52983.2022.9961011
Hang Du, Jun Yan, Mohsen Ghafouri, Rawad F. Zgheib, M. Debbabi
The retirement of synchronous generators and the increasing capacity of renewable energy sources (RES) have contributed to the decline of system strength in a power grid, leading to a problem such as subsynchronous oscillation (SSO) in permanent magnet synchronous generator (PMSG)-based wind farms. Cyberattacks aimed at reducing the system strength of the power grid may exacerbate this problem. However, existing solutions are either incomplete for system strength evaluation or exclude the consideration of cyberattacks. To this end, this paper presents a comprehensive system strength evaluation and a short-term system strength prediction process that takes into account the stealthy cyberattacks launched especially when the system strength provision is at its minimum level. In addition, proactive risk management is proposed to retain the grid's system strength for the wind farm above the minimum required level even if a cyberattack takes place. The efficacy of the proposed system strength evaluation and risk management based on system strength prediction is demonstrated through case studies in the IEEE 9-bus benchmark.
{"title":"Online Attack-aware Risk Management for PMSG-based Wind Farm Depending on System Strength Evaluation","authors":"Hang Du, Jun Yan, Mohsen Ghafouri, Rawad F. Zgheib, M. Debbabi","doi":"10.1109/SmartGridComm52983.2022.9961011","DOIUrl":"https://doi.org/10.1109/SmartGridComm52983.2022.9961011","url":null,"abstract":"The retirement of synchronous generators and the increasing capacity of renewable energy sources (RES) have contributed to the decline of system strength in a power grid, leading to a problem such as subsynchronous oscillation (SSO) in permanent magnet synchronous generator (PMSG)-based wind farms. Cyberattacks aimed at reducing the system strength of the power grid may exacerbate this problem. However, existing solutions are either incomplete for system strength evaluation or exclude the consideration of cyberattacks. To this end, this paper presents a comprehensive system strength evaluation and a short-term system strength prediction process that takes into account the stealthy cyberattacks launched especially when the system strength provision is at its minimum level. In addition, proactive risk management is proposed to retain the grid's system strength for the wind farm above the minimum required level even if a cyberattack takes place. The efficacy of the proposed system strength evaluation and risk management based on system strength prediction is demonstrated through case studies in the IEEE 9-bus benchmark.","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":"122394262","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/SmartGridComm52983.2022.9961036
P. Gope, B. Sikdar
Smart meters play an important role in modern power grids by providing fine-grained power consumption data and enabling services such as dynamic pricing and demand-side management. The smart metering devices are firmware-driven, where it is important that the devices be able to securely update their firmware on a regular basis to fix bugs, and improve as well as add services. In this paper, we propose a new privacy-aware secure firmware-updating framework called PRSUF (Privacy-aware Reconfigurable Secure-Firmware Updating Framework) to securely update the firmware in smart metering devices. The proposed the framework allows a hardware intrinsic secret to being updated and stored in a secure and efficient way. One of its key differentiating features is that, unlike existing mechanisms, the proposed scheme does not require storing any keys in the meter's non-volatile memory (NVM), thereby making it is secure against a number of physical and side-channel attacks. As compared to state-of-the-art solutions, the proposed security framework has notable features such as reconfigurability, protection against cloning and downgrading, detection of theft of services and tampering with the firmware and the hardware, etc.
{"title":"A Reconfigurable and Secure Firmware Updating Framework for Advanced Metering Infrastructure","authors":"P. Gope, B. Sikdar","doi":"10.1109/SmartGridComm52983.2022.9961036","DOIUrl":"https://doi.org/10.1109/SmartGridComm52983.2022.9961036","url":null,"abstract":"Smart meters play an important role in modern power grids by providing fine-grained power consumption data and enabling services such as dynamic pricing and demand-side management. The smart metering devices are firmware-driven, where it is important that the devices be able to securely update their firmware on a regular basis to fix bugs, and improve as well as add services. In this paper, we propose a new privacy-aware secure firmware-updating framework called PRSUF (Privacy-aware Reconfigurable Secure-Firmware Updating Framework) to securely update the firmware in smart metering devices. The proposed the framework allows a hardware intrinsic secret to being updated and stored in a secure and efficient way. One of its key differentiating features is that, unlike existing mechanisms, the proposed scheme does not require storing any keys in the meter's non-volatile memory (NVM), thereby making it is secure against a number of physical and side-channel attacks. As compared to state-of-the-art solutions, the proposed security framework has notable features such as reconfigurability, protection against cloning and downgrading, detection of theft of services and tampering with the firmware and the hardware, etc.","PeriodicalId":252202,"journal":{"name":"2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"97 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":"127999427","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/SmartGridComm52983.2022.9961001
Jia Wei Teo, Sean Gunawan, P. Biswas, D. Mashima
Electrical substations in power grid act as the critical interface points for the transmission and distribution networks. Over the years, digital technology has been integrated into the substations for remote control and automation. As a result, substations are more prone to cyber attacks and exposed to digital vulnerabilities. One of the notable cyber attack vectors is the malicious command injection, which can lead to shutting down of substations and subsequently power outages as demonstrated in Ukraine Power Plant Attack in 2015. Prevailing measures based on cyber rules (e.g., firewalls and intrusion detection systems) are often inadequate to detect advanced and stealthy attacks that use legitimate-looking measurements or control messages to cause physical damage. Additionally, defenses that use physics-based approaches (e.g., power flow simulation, state estimation, etc.) to detect malicious commands suffer from high latency. Machine learning serves as a potential solution in detecting command injection attacks with high accuracy and low latency. However, sufficient datasets are not readily available to train and evaluate the machine learning models. In this paper, focusing on this particular challenge, we discuss various approaches for the generation of synthetic data that can be used to train the machine learning models. Further, we evaluate the models trained with the synthetic data against attack datasets that simulates malicious commands injections with different levels of sophistication. Our findings show that synthetic data generated with some level of power grid domain knowledge helps train robust machine learning models against different types of attacks.
{"title":"Evaluating Synthetic Datasets for Training Machine Learning Models to Detect Malicious Commands","authors":"Jia Wei Teo, Sean Gunawan, P. Biswas, D. Mashima","doi":"10.1109/SmartGridComm52983.2022.9961001","DOIUrl":"https://doi.org/10.1109/SmartGridComm52983.2022.9961001","url":null,"abstract":"Electrical substations in power grid act as the critical interface points for the transmission and distribution networks. Over the years, digital technology has been integrated into the substations for remote control and automation. As a result, substations are more prone to cyber attacks and exposed to digital vulnerabilities. One of the notable cyber attack vectors is the malicious command injection, which can lead to shutting down of substations and subsequently power outages as demonstrated in Ukraine Power Plant Attack in 2015. Prevailing measures based on cyber rules (e.g., firewalls and intrusion detection systems) are often inadequate to detect advanced and stealthy attacks that use legitimate-looking measurements or control messages to cause physical damage. Additionally, defenses that use physics-based approaches (e.g., power flow simulation, state estimation, etc.) to detect malicious commands suffer from high latency. Machine learning serves as a potential solution in detecting command injection attacks with high accuracy and low latency. However, sufficient datasets are not readily available to train and evaluate the machine learning models. In this paper, focusing on this particular challenge, we discuss various approaches for the generation of synthetic data that can be used to train the machine learning models. Further, we evaluate the models trained with the synthetic data against attack datasets that simulates malicious commands injections with different levels of sophistication. Our findings show that synthetic data generated with some level of power grid domain knowledge helps train robust machine learning models against different types of attacks.","PeriodicalId":252202,"journal":{"name":"2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"81 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":"121530003","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：