Pub Date : 2022-10-25DOI: 10.1109/SmartGridComm52983.2022.9960999
Qisheng Huang, Yunshu Liu, Peng-jie Sun, Junling Li, Jin Xu
Many countries have implemented different policies to achieve carbon neutrality in the current century. The cap-and-trade policy is one of the popular policies. The cap-and-trade policy provides carbon emission quotas for power generation companies. Each company must carefully determine its energy production based on the carbon emission quota and demand uncertainty. In this paper, we analyze the cooperation among different power generation companies using the coalitional game theory. We show the optimality of the grand coalition for minimizing the total cost by proving that the cost function is subadditive. This result highlights the benefits of cooperation. We further propose a cost allocation mechanism that allocates the total cost to different power generation companies. We prove that the proposed cost allocation mechanism is in the core of the coalitional game such that no group of power generation companies has any incentive to leave the grand coalition. Numerical experiments have been conducted to validate the established theoretical results.
{"title":"Cooperative Carbon Emission Trading: A Coalitional Game Approach","authors":"Qisheng Huang, Yunshu Liu, Peng-jie Sun, Junling Li, Jin Xu","doi":"10.1109/SmartGridComm52983.2022.9960999","DOIUrl":"https://doi.org/10.1109/SmartGridComm52983.2022.9960999","url":null,"abstract":"Many countries have implemented different policies to achieve carbon neutrality in the current century. The cap-and-trade policy is one of the popular policies. The cap-and-trade policy provides carbon emission quotas for power generation companies. Each company must carefully determine its energy production based on the carbon emission quota and demand uncertainty. In this paper, we analyze the cooperation among different power generation companies using the coalitional game theory. We show the optimality of the grand coalition for minimizing the total cost by proving that the cost function is subadditive. This result highlights the benefits of cooperation. We further propose a cost allocation mechanism that allocates the total cost to different power generation companies. We prove that the proposed cost allocation mechanism is in the core of the coalitional game such that no group of power generation companies has any incentive to leave the grand coalition. Numerical experiments have been conducted to validate the established theoretical results.","PeriodicalId":252202,"journal":{"name":"2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"274 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":"134448251","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/SmartGridComm52983.2022.9961052
Utku Tefek, Ertem Esiner, D. Mashima, Yih-Chun Hu
An inevitable consequence of automated control and communication in electric substations is the vulnerability against cyberattacks that compromise the integrity and authenticity of messages. IEC 62351 standard stipulates the use of message authentication solutions, although there is no firm guidance on the exact method to be adopted. The earlier IEC 62351-6:2007 standard recommended the use of digital signatures. However, digital signatures do not meet the timing requirements of IEC 61850 GOOSE and SV. Thus, the recent revisions to IEC 62351–6 backtracked from digital signatures in favor of message authentication code (MAC) algorithms, thereby sacrificing key properties, i.e., scaling well for multiple destinations, easy key distribution and management, public verifiability, and non-repudiation. Following these revisions, tailoring MAC-based algorithms for IEC 61850 message structure has gained traction. Additionally, new message authentication solutions that exploit the small or low entropy messages, such as those in GOOSE and SV, have been proposed to secure time-critical communication. These solutions retain certain key properties of digital signatures within the delay requirements of GOOSE and SV. This paper addresses the key trade-offs and discusses the feasibility of the promising message authentication solutions for IEC 61850 GOOSE and SV. Through their implementation on a low-cost hardware BeagleBoard-X15 we report on the real-world comparison of performance metrics.
{"title":"Analysis of Message Authentication Solutions for IEC 61850 in Substation Automation Systems","authors":"Utku Tefek, Ertem Esiner, D. Mashima, Yih-Chun Hu","doi":"10.1109/SmartGridComm52983.2022.9961052","DOIUrl":"https://doi.org/10.1109/SmartGridComm52983.2022.9961052","url":null,"abstract":"An inevitable consequence of automated control and communication in electric substations is the vulnerability against cyberattacks that compromise the integrity and authenticity of messages. IEC 62351 standard stipulates the use of message authentication solutions, although there is no firm guidance on the exact method to be adopted. The earlier IEC 62351-6:2007 standard recommended the use of digital signatures. However, digital signatures do not meet the timing requirements of IEC 61850 GOOSE and SV. Thus, the recent revisions to IEC 62351–6 backtracked from digital signatures in favor of message authentication code (MAC) algorithms, thereby sacrificing key properties, i.e., scaling well for multiple destinations, easy key distribution and management, public verifiability, and non-repudiation. Following these revisions, tailoring MAC-based algorithms for IEC 61850 message structure has gained traction. Additionally, new message authentication solutions that exploit the small or low entropy messages, such as those in GOOSE and SV, have been proposed to secure time-critical communication. These solutions retain certain key properties of digital signatures within the delay requirements of GOOSE and SV. This paper addresses the key trade-offs and discusses the feasibility of the promising message authentication solutions for IEC 61850 GOOSE and SV. Through their implementation on a low-cost hardware BeagleBoard-X15 we report on the real-world comparison of performance metrics.","PeriodicalId":252202,"journal":{"name":"2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"8 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":"114333831","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/SmartGridComm52983.2022.9961056
D. Nicol, Emily Belovich, Atul Bohara
We describe BPC, an open source tool and a library of best-practice rules for configuration of smart grid communication networks and the flows they carry. We describe the kinds of rules BPC presently includes, the format of expressing best-practices rules, the way that BPC performs its evaluation and reporting, and application to a case study from a utility's network.
{"title":"Smart Grid Network Flows Best Practices Checker","authors":"D. Nicol, Emily Belovich, Atul Bohara","doi":"10.1109/SmartGridComm52983.2022.9961056","DOIUrl":"https://doi.org/10.1109/SmartGridComm52983.2022.9961056","url":null,"abstract":"We describe BPC, an open source tool and a library of best-practice rules for configuration of smart grid communication networks and the flows they carry. We describe the kinds of rules BPC presently includes, the format of expressing best-practices rules, the way that BPC performs its evaluation and reporting, and application to a case study from a utility's network.","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":"115675017","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/SmartGridComm52983.2022.9961051
Guangjun Huang, A. Anwar, S. Loke, A. Zaslavsky, Jinho Choi
Sustainable use of energy requires to achieve optimal energy utilization in smart grid systems. It is possible by empowering the Internet of Things (IoT) based Wireless connectivity through real-time energy monitoring and analyses of power consumption patterns. Modeling optimal energy utilization considering multi-user behaviors is particularly challenging in such context. To address the challenge of one-to-one-mapping of energy disaggregation in device-sharing environments by multiple co-existing users, a new method based on data-driven machine learning (e.g., individual energy usage pattern analysis) is proposed in this paper that aims to accurately match the energy consumption of electrical appliances with specific users. In particular, the machine learning model with the best performance is selected for real-time energy/power disaggregation on the local server (i.e., small-scale home/office) to ensure comparable or better performance with state-of-the-art disaggregation algorithms. In addition, energy usage patterns and individual power consumption data are analyzed comprehensively to match overall energy consumption and label datasets by events. Distributed learning is also discussed to exploit other local servers' datasets for better disaggregation through IoT networks. The effectiveness of the proposed method is verified by using simulated datasets in a motivating scenario.
{"title":"Smart Home/Office Energy Management based on Individual Data Analysis through IoT Networks","authors":"Guangjun Huang, A. Anwar, S. Loke, A. Zaslavsky, Jinho Choi","doi":"10.1109/SmartGridComm52983.2022.9961051","DOIUrl":"https://doi.org/10.1109/SmartGridComm52983.2022.9961051","url":null,"abstract":"Sustainable use of energy requires to achieve optimal energy utilization in smart grid systems. It is possible by empowering the Internet of Things (IoT) based Wireless connectivity through real-time energy monitoring and analyses of power consumption patterns. Modeling optimal energy utilization considering multi-user behaviors is particularly challenging in such context. To address the challenge of one-to-one-mapping of energy disaggregation in device-sharing environments by multiple co-existing users, a new method based on data-driven machine learning (e.g., individual energy usage pattern analysis) is proposed in this paper that aims to accurately match the energy consumption of electrical appliances with specific users. In particular, the machine learning model with the best performance is selected for real-time energy/power disaggregation on the local server (i.e., small-scale home/office) to ensure comparable or better performance with state-of-the-art disaggregation algorithms. In addition, energy usage patterns and individual power consumption data are analyzed comprehensively to match overall energy consumption and label datasets by events. Distributed learning is also discussed to exploit other local servers' datasets for better disaggregation through IoT networks. The effectiveness of the proposed method is verified by using simulated datasets in a motivating scenario.","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":"114409552","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/SmartGridComm52983.2022.9961017
Hanyu Zeng, Zhen Wei Ng, Pengfei Zhou, Xin Lou, David K. Y. Yau, M. Winslett
Modern power grids are undergoing significant changes driven by information and communication technologies (ICTs), and evolving into smart grids with higher efficiency and lower operation cost. Using ICTs, however, comes with an inevitable side effect that makes the power system more vulnerable to cyber attacks. In this paper, we propose a self-supervised learning-based framework to detect and identify various types of cyber attacks. Different from existing approaches, the proposed framework does not rely on large amounts of well-curated labeled data but makes use of the massive unlabeled data in the wild which are easily accessible. Specifically, the proposed framework adopts the BERT model from the natural language processing domain and learns generalizable and effective representations from the unlabeled sensing data, which capture the distinctive patterns of different attacks. Using the learned representations, together with a very small amount of labeled data, we can train a task-specific classifier to detect various types of cyber attacks. Experiment results in a 3-area power grid system with 37 buses demonstrate the superior performance of our framework over existing approaches, especially when a very limited amount of labeled data are available. We believe such a framework can be easily adopted to detect a variety of cyber attacks in other power grid scenarios.
{"title":"Detecting Cyber Attacks in Smart Grids with Massive Unlabeled Sensing Data","authors":"Hanyu Zeng, Zhen Wei Ng, Pengfei Zhou, Xin Lou, David K. Y. Yau, M. Winslett","doi":"10.1109/SmartGridComm52983.2022.9961017","DOIUrl":"https://doi.org/10.1109/SmartGridComm52983.2022.9961017","url":null,"abstract":"Modern power grids are undergoing significant changes driven by information and communication technologies (ICTs), and evolving into smart grids with higher efficiency and lower operation cost. Using ICTs, however, comes with an inevitable side effect that makes the power system more vulnerable to cyber attacks. In this paper, we propose a self-supervised learning-based framework to detect and identify various types of cyber attacks. Different from existing approaches, the proposed framework does not rely on large amounts of well-curated labeled data but makes use of the massive unlabeled data in the wild which are easily accessible. Specifically, the proposed framework adopts the BERT model from the natural language processing domain and learns generalizable and effective representations from the unlabeled sensing data, which capture the distinctive patterns of different attacks. Using the learned representations, together with a very small amount of labeled data, we can train a task-specific classifier to detect various types of cyber attacks. Experiment results in a 3-area power grid system with 37 buses demonstrate the superior performance of our framework over existing approaches, especially when a very limited amount of labeled data are available. We believe such a framework can be easily adopted to detect a variety of cyber attacks in other power grid scenarios.","PeriodicalId":252202,"journal":{"name":"2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"22 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":"123930754","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/SmartGridComm52983.2022.9961003
Luca Maria Castiglione, Zhongyuan Hau, Pudong Get, Kenneth T. Co, Luis Muñoz-González, F. Teng, Emil C. Lupu
Attacks targeting smart grid infrastructures can result in the disruptions of power supply as well as damages to costly equipment, with significant impact on safety as well as on end-consumers. It is therefore of essence to identify attack paths in the infrastructure that lead to safety violations and to determine critical components that must be protected. In this paper, we introduce a methodology (HA-Grid) that incorporates both safety and security modelling of smart grid infrastructure to analyse the impact of cyber threats on the safety of smart grid infrastructures. HA-Grid is applied on a smart grid test-bed to identify attack paths that lead to safety hazards, and to determine the common nodes in these attack paths as critical components that must be protected.
{"title":"HA-Grid: Security Aware Hazard Analysis for Smart Grids","authors":"Luca Maria Castiglione, Zhongyuan Hau, Pudong Get, Kenneth T. Co, Luis Muñoz-González, F. Teng, Emil C. Lupu","doi":"10.1109/SmartGridComm52983.2022.9961003","DOIUrl":"https://doi.org/10.1109/SmartGridComm52983.2022.9961003","url":null,"abstract":"Attacks targeting smart grid infrastructures can result in the disruptions of power supply as well as damages to costly equipment, with significant impact on safety as well as on end-consumers. It is therefore of essence to identify attack paths in the infrastructure that lead to safety violations and to determine critical components that must be protected. In this paper, we introduce a methodology (HA-Grid) that incorporates both safety and security modelling of smart grid infrastructure to analyse the impact of cyber threats on the safety of smart grid infrastructures. HA-Grid is applied on a smart grid test-bed to identify attack paths that lead to safety hazards, and to determine the common nodes in these attack paths as critical components that must be protected.","PeriodicalId":252202,"journal":{"name":"2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"12 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":"116221675","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 : 2022-10-11DOI: 10.1109/SmartGridComm52983.2022.9960965
S. Sourav, P. Biswas, Binbin Chen, D. Mashima
In modern smart grids, the proliferation of communication enabled distributed energy resource (DER) systems has increased the surface of possible cyber-physical attacks. Attacks originating from the distributed edge devices of DER system, such as photovoltaic (PV) system, is often difficult to detect. An attacker may change the control configurations or various setpoints of the PV inverters to destabilize the power grid, damage devices, or for the purpose of economic gain. A more powerful attacker may even manipulate the PV system metering data transmitted for remote monitoring, so that (s)he can remain hidden. In this paper, we consider a case where PV systems operating in different control modes can be simultaneously attacked and the attacker has the ability to manipulate individual PV bus measurements to avoid detection. We show that even in such a scenario, with just the aggregated measurements (that the attacker cannot manipulate), machine learning (ML) techniques are able to detect the attack in a fast and accurate manner. We use a standard radial distribution network, together with real smart home electricity consumption data and solar power data in our experimental setup. We test the performance of several ML algorithms to detect attacks on the PV system. Our detailed evaluations show that the proposed intrusion detection system (IDS) is highly effective and efficient in detecting attacks on PV inverter control modes.
{"title":"Detecting Hidden Attackers in Photovoltaic Systems Using Machine Learning","authors":"S. Sourav, P. Biswas, Binbin Chen, D. Mashima","doi":"10.1109/SmartGridComm52983.2022.9960965","DOIUrl":"https://doi.org/10.1109/SmartGridComm52983.2022.9960965","url":null,"abstract":"In modern smart grids, the proliferation of communication enabled distributed energy resource (DER) systems has increased the surface of possible cyber-physical attacks. Attacks originating from the distributed edge devices of DER system, such as photovoltaic (PV) system, is often difficult to detect. An attacker may change the control configurations or various setpoints of the PV inverters to destabilize the power grid, damage devices, or for the purpose of economic gain. A more powerful attacker may even manipulate the PV system metering data transmitted for remote monitoring, so that (s)he can remain hidden. In this paper, we consider a case where PV systems operating in different control modes can be simultaneously attacked and the attacker has the ability to manipulate individual PV bus measurements to avoid detection. We show that even in such a scenario, with just the aggregated measurements (that the attacker cannot manipulate), machine learning (ML) techniques are able to detect the attack in a fast and accurate manner. We use a standard radial distribution network, together with real smart home electricity consumption data and solar power data in our experimental setup. We test the performance of several ML algorithms to detect attacks on the PV system. Our detailed evaluations show that the proposed intrusion detection system (IDS) is highly effective and efficient in detecting attacks on PV inverter control modes.","PeriodicalId":252202,"journal":{"name":"2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126809322","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}
This paper examines the problem of optimizing the charging pattern of electric vehicles (EV) by taking real-time electricity grid carbon intensity into consideration. The objective of the proposed charging scheme is to minimize the carbon emissions contributed by EV charging events, while simultaneously satisfying constraints posed by EV user's charging schedules, charging station transformer limits, and battery physical constraints. Using real-world EV charging data and California electricity generation records, this paper shows that our carbon-aware real-time charging scheme saves an average of 3.81% of carbon emission while delivering satisfactory amount of energy. Furthermore, by using an adaptive balanced factor, we can reduce 26.00% of carbon emission on average while compromising 12.61% of total energy delivered.
{"title":"Carbon-Aware EV Charging","authors":"Kai-wen Cheng, Yuexin Bian, Yuanyuan Shi, Yize Chen","doi":"10.1109/SmartGridComm52983.2022.9960988","DOIUrl":"https://doi.org/10.1109/SmartGridComm52983.2022.9960988","url":null,"abstract":"This paper examines the problem of optimizing the charging pattern of electric vehicles (EV) by taking real-time electricity grid carbon intensity into consideration. The objective of the proposed charging scheme is to minimize the carbon emissions contributed by EV charging events, while simultaneously satisfying constraints posed by EV user's charging schedules, charging station transformer limits, and battery physical constraints. Using real-world EV charging data and California electricity generation records, this paper shows that our carbon-aware real-time charging scheme saves an average of 3.81% of carbon emission while delivering satisfactory amount of energy. Furthermore, by using an adaptive balanced factor, we can reduce 26.00% of carbon emission on average while compromising 12.61% of total energy delivered.","PeriodicalId":252202,"journal":{"name":"2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129527900","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 : 2022-09-23DOI: 10.1109/SmartGridComm52983.2022.9961002
Alexander Beattie, Pavol Mulinka, Subham S. Sahoo, I. Christou, Charalampos Kalalas, Daniel Gutierrez-Rojas, P. Nardelli
Timely and accurate detection of anomalies in power electronics is becoming increasingly critical for maintaining complex production systems. Robust and explainable strategies help decrease system downtime and preempt or mitigate infrastructure cyberattacks. This work begins by explaining the types of uncertainty present in current datasets and machine learning algorithm outputs. Three techniques for combating these uncertainties are then introduced and analyzed. We further present two anomaly detection and classification approaches, namely the Matrix Profile algorithm and anomaly transformer, which are applied in the context of a power electronic converter dataset. Specifically, the Matrix Profile algorithm is shown to be well suited as a generalizable approach for detecting real-time anomalies in streaming time-series data. The STUMPY python library implementation of the iterative Matrix Profile is used for the creation of the detector. A series of custom filters is created and added to the detector to tune its sensitivity, recall, and detection accuracy. Our numerical results show that, with simple parameter tuning, the detector provides high accuracy and performance in a variety of fault scenarios.
{"title":"A Robust and Explainable Data-Driven Anomaly Detection Approach For Power Electronics","authors":"Alexander Beattie, Pavol Mulinka, Subham S. Sahoo, I. Christou, Charalampos Kalalas, Daniel Gutierrez-Rojas, P. Nardelli","doi":"10.1109/SmartGridComm52983.2022.9961002","DOIUrl":"https://doi.org/10.1109/SmartGridComm52983.2022.9961002","url":null,"abstract":"Timely and accurate detection of anomalies in power electronics is becoming increasingly critical for maintaining complex production systems. Robust and explainable strategies help decrease system downtime and preempt or mitigate infrastructure cyberattacks. This work begins by explaining the types of uncertainty present in current datasets and machine learning algorithm outputs. Three techniques for combating these uncertainties are then introduced and analyzed. We further present two anomaly detection and classification approaches, namely the Matrix Profile algorithm and anomaly transformer, which are applied in the context of a power electronic converter dataset. Specifically, the Matrix Profile algorithm is shown to be well suited as a generalizable approach for detecting real-time anomalies in streaming time-series data. The STUMPY python library implementation of the iterative Matrix Profile is used for the creation of the detector. A series of custom filters is created and added to the detector to tune its sensitivity, recall, and detection accuracy. Our numerical results show that, with simple parameter tuning, the detector provides high accuracy and performance in a variety of fault scenarios.","PeriodicalId":252202,"journal":{"name":"2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125768459","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 : 2022-09-21DOI: 10.1109/SmartGridComm52983.2022.9960994
Dongwei Zhao, Hao Wang, Jianwei Huang, Xiaojun Lin
The increasing penetration of renewable energy poses significant challenges to power grid reliability. There have been increasing interests in utilizing financial tools, such as insurance, to help end-users hedge the potential risk of lost load due to renewable energy variability. With insurance, a user pays a premium fee to the utility, so that he will get compensated in case his demand is not fully satisfied. A proper insurance design needs to resolve the following two challenges: (i) users' reliability preference is private information; and (ii) the insurance design is tightly coupled with the renewable energy investment decision. To address these challenges, we adopt the contract theory to elicit users' private reliability preferences, and we study how the utility can jointly optimize the insurance contract and the planning of renewable energy. A key analytical challenge is that the joint optimization of the insurance design and the planning of renewables is non-convex. We resolve this difficulty by revealing important structural properties of the optimal solution, using the help of two benchmark problems: the no-insurance benchmark and the social-optimum benchmark. Compared with the no-insurance benchmark, we prove that the social cost and users' total energy cost are always no larger under the optimal contract. Simulation results show that the largest benefit of the insurance contract is achieved at a medium electricity-bill price together with a low type heterogeneity and a high renewable uncertainty.
{"title":"Insurance Contract for High Renewable Energy Integration","authors":"Dongwei Zhao, Hao Wang, Jianwei Huang, Xiaojun Lin","doi":"10.1109/SmartGridComm52983.2022.9960994","DOIUrl":"https://doi.org/10.1109/SmartGridComm52983.2022.9960994","url":null,"abstract":"The increasing penetration of renewable energy poses significant challenges to power grid reliability. There have been increasing interests in utilizing financial tools, such as insurance, to help end-users hedge the potential risk of lost load due to renewable energy variability. With insurance, a user pays a premium fee to the utility, so that he will get compensated in case his demand is not fully satisfied. A proper insurance design needs to resolve the following two challenges: (i) users' reliability preference is private information; and (ii) the insurance design is tightly coupled with the renewable energy investment decision. To address these challenges, we adopt the contract theory to elicit users' private reliability preferences, and we study how the utility can jointly optimize the insurance contract and the planning of renewable energy. A key analytical challenge is that the joint optimization of the insurance design and the planning of renewables is non-convex. We resolve this difficulty by revealing important structural properties of the optimal solution, using the help of two benchmark problems: the no-insurance benchmark and the social-optimum benchmark. Compared with the no-insurance benchmark, we prove that the social cost and users' total energy cost are always no larger under the optimal contract. Simulation results show that the largest benefit of the insurance contract is achieved at a medium electricity-bill price together with a low type heterogeneity and a high renewable uncertainty.","PeriodicalId":252202,"journal":{"name":"2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"08 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127177027","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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