Pub Date : 2021-10-25DOI: 10.1109/SmartGridComm51999.2021.9632326
Mikhak Samadi, H. Schriemer, S. Ruj, M. Erol-Kantarci
The high impact of demand reduction on the energy grid management and the importance of reducing loss of distributed energy resources (DERs), in addition to the necessity of a secure distributed data storing system motivate us to propose an energy blockchain solution. This paper presents a demand response (DR) solution utilizing energy blockchain to reduce demand, save the extra DERs, and efficiently incorporate customers block mining ability. In this work, a real dataset of customer demand profiles and PV generation in the Ottawa region is used to deploy a DR Stackelberg game between a control agent (CA) and local customers to negotiate demand reduction by integrating the block mining method as DERs saving. This article presents a novel and well-suited consensus algorithm, Proof of Energy Saving (PoES), that is used to incentivize the customers to reduce their demand, discharge their electric vehicle (EV) and maximize their chance for block mining to earn monetary rewards and DER savings. This results in lower peak demand, customer bill reduction, and transforms energy savings into monetary resources. Furthermore, the results show that our proposed consensus algorithm is robust and secure against malicious actions of users.
{"title":"Energy Blockchain for Demand Response and Distributed Energy Resource Management","authors":"Mikhak Samadi, H. Schriemer, S. Ruj, M. Erol-Kantarci","doi":"10.1109/SmartGridComm51999.2021.9632326","DOIUrl":"https://doi.org/10.1109/SmartGridComm51999.2021.9632326","url":null,"abstract":"The high impact of demand reduction on the energy grid management and the importance of reducing loss of distributed energy resources (DERs), in addition to the necessity of a secure distributed data storing system motivate us to propose an energy blockchain solution. This paper presents a demand response (DR) solution utilizing energy blockchain to reduce demand, save the extra DERs, and efficiently incorporate customers block mining ability. In this work, a real dataset of customer demand profiles and PV generation in the Ottawa region is used to deploy a DR Stackelberg game between a control agent (CA) and local customers to negotiate demand reduction by integrating the block mining method as DERs saving. This article presents a novel and well-suited consensus algorithm, Proof of Energy Saving (PoES), that is used to incentivize the customers to reduce their demand, discharge their electric vehicle (EV) and maximize their chance for block mining to earn monetary rewards and DER savings. This results in lower peak demand, customer bill reduction, and transforms energy savings into monetary resources. Furthermore, the results show that our proposed consensus algorithm is robust and secure against malicious actions of users.","PeriodicalId":378884,"journal":{"name":"2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134531403","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 : 2021-10-25DOI: 10.1109/SmartGridComm51999.2021.9632309
Chenxi Sun, Tongxin Li, Xiaoying Tang
It is believed that Electric Vehicles (EVs) will play an increasingly important role in making the city greener and smarter. However, a critical challenge raised by the transportation electrification process is the proper planning of city-wide EV charging infrastructures, i.e., the siting and sizing of charging stations, especially for the cities that just start promoting the adoption of EVs. In this paper, we investigate the following problem: For a city with a limited budget for public EV charging infrastructure construction, where should the charging stations be deployed to promote the transition of EVs from traditional cars? We propose a δ-nearest model that captures people's satisfaction towards a certain design and formulate the EV charging station placement problem as a monotone submodular maximization problem, equipped with gridded population data and trip data. We then propose a greedy-based algorithm to solve the problem efficiently with a provable approximation ratio. A case study using fine-grained Haikou population data, Point of Interest (POI) data, and trip data is also provided to demonstrate the effectiveness of our approach.
{"title":"Data-driven Electric Vehicle Charging Station Placement for Incentivizing Potential Demand","authors":"Chenxi Sun, Tongxin Li, Xiaoying Tang","doi":"10.1109/SmartGridComm51999.2021.9632309","DOIUrl":"https://doi.org/10.1109/SmartGridComm51999.2021.9632309","url":null,"abstract":"It is believed that Electric Vehicles (EVs) will play an increasingly important role in making the city greener and smarter. However, a critical challenge raised by the transportation electrification process is the proper planning of city-wide EV charging infrastructures, i.e., the siting and sizing of charging stations, especially for the cities that just start promoting the adoption of EVs. In this paper, we investigate the following problem: For a city with a limited budget for public EV charging infrastructure construction, where should the charging stations be deployed to promote the transition of EVs from traditional cars? We propose a δ-nearest model that captures people's satisfaction towards a certain design and formulate the EV charging station placement problem as a monotone submodular maximization problem, equipped with gridded population data and trip data. We then propose a greedy-based algorithm to solve the problem efficiently with a provable approximation ratio. A case study using fine-grained Haikou population data, Point of Interest (POI) data, and trip data is also provided to demonstrate the effectiveness of our approach.","PeriodicalId":378884,"journal":{"name":"2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129281124","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 : 2021-10-25DOI: 10.1109/SmartGridComm51999.2021.9632314
Yu He, F. Luo, G. Ranzi, Weicong Kong
Power load forecasting plays a fundamental role in modern energy systems' operations. While traditional load forecasting applies to bus-level aggregated load data, widespread deployment of advanced metering infrastructure creates an opportunity to fine-grained monitor the power consumption of single households and to predict their load requirements. This paper proposes a distributed residential load forecasting framework that combines federated learning and load clustering techniques. The system firstly applies a K-means clustering algorithm to divide a group of residential users into multiple clusters based on their historical power consumption patterns. For each cluster, the system then applies a federated learning process to enable the users in that cluster to collaboratively train their local load prediction models without physically sharing their load data. Experiments and comparison studies are conducted based on a real Australian residential load dataset to validate the proposed approach and to highlight its ease of use.
{"title":"Short-Term Residential Load Forecasting Based on Federated Learning and Load Clustering","authors":"Yu He, F. Luo, G. Ranzi, Weicong Kong","doi":"10.1109/SmartGridComm51999.2021.9632314","DOIUrl":"https://doi.org/10.1109/SmartGridComm51999.2021.9632314","url":null,"abstract":"Power load forecasting plays a fundamental role in modern energy systems' operations. While traditional load forecasting applies to bus-level aggregated load data, widespread deployment of advanced metering infrastructure creates an opportunity to fine-grained monitor the power consumption of single households and to predict their load requirements. This paper proposes a distributed residential load forecasting framework that combines federated learning and load clustering techniques. The system firstly applies a K-means clustering algorithm to divide a group of residential users into multiple clusters based on their historical power consumption patterns. For each cluster, the system then applies a federated learning process to enable the users in that cluster to collaboratively train their local load prediction models without physically sharing their load data. Experiments and comparison studies are conducted based on a real Australian residential load dataset to validate the proposed approach and to highlight its ease of use.","PeriodicalId":378884,"journal":{"name":"2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127502491","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 : 2021-10-25DOI: 10.1109/SmartGridComm51999.2021.9632321
Sina Hassani, J. Bendtsen, R. Olsen
Penetration of distributed generation into distribution grids brings new demands for both centralized and distributed control at the low-voltage level. In particular, when trying to coordinate the production from distributed generation, communication becomes an important aspect of control design. However, whereas local control typically occurs at sub-second resolution, communication between geographically separate locations based on e.g., smart meter data, commonly takes place at much lower frequencies, such as on an hourly basis or even slower. Therefore, novel distribution grids should be analyzed and controlled within the context of cyber-physical systems. Hybrid systems, which cover systems that have both continuous and discrete dynamics, provide the natural setting for such analysis. In this paper, a hybrid model of the distribution grid considering both the continuous states of the power network and the discrete nature of the communication is presented, capturing the different update rates of centralized and local controllers in the modeling process. Simulation results show good agreement with data from a real-life system.
{"title":"Hybrid Modeling of Cyber-Physical Distribution Grids","authors":"Sina Hassani, J. Bendtsen, R. Olsen","doi":"10.1109/SmartGridComm51999.2021.9632321","DOIUrl":"https://doi.org/10.1109/SmartGridComm51999.2021.9632321","url":null,"abstract":"Penetration of distributed generation into distribution grids brings new demands for both centralized and distributed control at the low-voltage level. In particular, when trying to coordinate the production from distributed generation, communication becomes an important aspect of control design. However, whereas local control typically occurs at sub-second resolution, communication between geographically separate locations based on e.g., smart meter data, commonly takes place at much lower frequencies, such as on an hourly basis or even slower. Therefore, novel distribution grids should be analyzed and controlled within the context of cyber-physical systems. Hybrid systems, which cover systems that have both continuous and discrete dynamics, provide the natural setting for such analysis. In this paper, a hybrid model of the distribution grid considering both the continuous states of the power network and the discrete nature of the communication is presented, capturing the different update rates of centralized and local controllers in the modeling process. Simulation results show good agreement with data from a real-life system.","PeriodicalId":378884,"journal":{"name":"2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124676286","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 : 2021-10-25DOI: 10.1109/SmartGridComm51999.2021.9632317
N. Shivaraman, Jakob Fittler, Saravanan Ramanathan, A. Easwaran, S. Steinhorst
The rapid proliferation of Internet-of-things (IoT) as well as mobile devices such as Electric Vehicles (EVs), has led to unpredictable load at the grid. The demand to supply ratio is particularly exacerbated at a few grid aggregators (charging stations) with excessive demand due to the geographic location, peak time, etc. Existing solutions on demand response cannot achieve significant improvements based only on time-shifting the loads. Device properties such as charging modes and movement capabilities can be exploited to enable geographic migration. Additionally, the information on the spare capacity at a few aggregators can aid in re-channeling the load from other aggregators facing excess demand to allow movement of devices. In this paper, we model these flexible properties of the devices as a mixed-integer non-linear problem (MINLP) to minimize excess load and improve the utility (benefit) across all devices. We propose an online distributed low-complexity heuristic that prioritizes devices based on demand and deadlines to minimize the cumulative loss in utility. The proposed heuristic is tested on an exhaustive set of synthetic data and compared with solutions from an optimization solver for the same runtime to show the impracticality of using a solver. Real-world EV testbed data was used to test our proposed solution and other scheduling solutions to show the practicality of generating a feasible schedule and a loss improvement of at least 57.23%.
{"title":"A novel load distribution strategy for aggregators using IoT-enabled mobile devices","authors":"N. Shivaraman, Jakob Fittler, Saravanan Ramanathan, A. Easwaran, S. Steinhorst","doi":"10.1109/SmartGridComm51999.2021.9632317","DOIUrl":"https://doi.org/10.1109/SmartGridComm51999.2021.9632317","url":null,"abstract":"The rapid proliferation of Internet-of-things (IoT) as well as mobile devices such as Electric Vehicles (EVs), has led to unpredictable load at the grid. The demand to supply ratio is particularly exacerbated at a few grid aggregators (charging stations) with excessive demand due to the geographic location, peak time, etc. Existing solutions on demand response cannot achieve significant improvements based only on time-shifting the loads. Device properties such as charging modes and movement capabilities can be exploited to enable geographic migration. Additionally, the information on the spare capacity at a few aggregators can aid in re-channeling the load from other aggregators facing excess demand to allow movement of devices. In this paper, we model these flexible properties of the devices as a mixed-integer non-linear problem (MINLP) to minimize excess load and improve the utility (benefit) across all devices. We propose an online distributed low-complexity heuristic that prioritizes devices based on demand and deadlines to minimize the cumulative loss in utility. The proposed heuristic is tested on an exhaustive set of synthetic data and compared with solutions from an optimization solver for the same runtime to show the impracticality of using a solver. Real-world EV testbed data was used to test our proposed solution and other scheduling solutions to show the practicality of generating a feasible schedule and a loss improvement of at least 57.23%.","PeriodicalId":378884,"journal":{"name":"2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115136811","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 : 2021-10-25DOI: 10.1109/SmartGridComm51999.2021.9631988
Yacoub Hanna, Mumin Cebe, Suat Mercan, K. Akkaya
As Smart Grid comes with new smart devices and additional data collection for improved control decisions, this puts a lot of burden on the underlying legacy communication infrastructures that may be severely limited in bandwidth. Therefore, an alternative is to consider publish-subscribe architectures for not only enabling flexible communication options but also exploiting multicasting capabilities to reduce the number of data messages transmitted. However, this capability needs to be complemented by a communication-efficient group key management scheme that will ensure security of multicast messages in terms of confidentiality, integrity and authentication. In this paper, we propose a group-key generation and renewal mechanism that minimizes the number of messages while still following the Diffie-Hellman (DH) Key exchange. Specifically, the Control Center (CC) utilizes Shamir's secret key sharing scheme to compute points for each device using random pairs sent by group members. Such points are then utilized to derive the group key based on Lagrange interpolation. The hash-chain concept is employed to renew the group key without requiring further message exchanges, essentially achieving key renewal in a single message. We evaluated our protocol by creating an MQTT-based testbed supporting multicasting. The results show that number of messages are decreased significantly compared to alternative approaches.
{"title":"Efficient Group-Key Management for Low-bandwidth Smart Grid Networks","authors":"Yacoub Hanna, Mumin Cebe, Suat Mercan, K. Akkaya","doi":"10.1109/SmartGridComm51999.2021.9631988","DOIUrl":"https://doi.org/10.1109/SmartGridComm51999.2021.9631988","url":null,"abstract":"As Smart Grid comes with new smart devices and additional data collection for improved control decisions, this puts a lot of burden on the underlying legacy communication infrastructures that may be severely limited in bandwidth. Therefore, an alternative is to consider publish-subscribe architectures for not only enabling flexible communication options but also exploiting multicasting capabilities to reduce the number of data messages transmitted. However, this capability needs to be complemented by a communication-efficient group key management scheme that will ensure security of multicast messages in terms of confidentiality, integrity and authentication. In this paper, we propose a group-key generation and renewal mechanism that minimizes the number of messages while still following the Diffie-Hellman (DH) Key exchange. Specifically, the Control Center (CC) utilizes Shamir's secret key sharing scheme to compute points for each device using random pairs sent by group members. Such points are then utilized to derive the group key based on Lagrange interpolation. The hash-chain concept is employed to renew the group key without requiring further message exchanges, essentially achieving key renewal in a single message. We evaluated our protocol by creating an MQTT-based testbed supporting multicasting. The results show that number of messages are decreased significantly compared to alternative approaches.","PeriodicalId":378884,"journal":{"name":"2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116812155","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 : 2021-10-21DOI: 10.1109/SmartGridComm51999.2021.9631994
Mostafa Mohammadpourfard, I. Genc, S. Lakshminarayana, Charalambos Konstantinou
Smart grid's objective is to enable electricity and information to flow two-way while providing effective, robust, computerized, and decentralized energy delivery. This necessitates the use of state estimation-based techniques and real-time analysis to ensure that effective controls are deployed properly. However, the reliance on communication technologies makes such systems susceptible to sophisticated data integrity attacks imposing serious threats to the overall reliability of smart grid. To detect such attacks, advanced and efficient anomaly detection solutions are needed. In this paper, a two-stage deep learning-based framework is carefully designed by embedding power system's characteristics enabling precise attack detection and localization. First, we encode temporal correlations of the multivariate power system time-series measurements as 2D images using image-based representation approaches such as Gramian Angular Field (GAF) and Recurrence Plot (RP) to obtain the latent data characteristics. These images are then utilized to build a highly reliable and resilient deep Convolutional Neural Network (CNN)-based multi-label classifier capable of learning both low and high level characteristics in the images to detect and discover the exact attack locations without leveraging any prior statistical assumptions. The proposed method is evaluated on the IEEE 57-bus system using real-world load data. Also, a comparative study is carried out. Numerical results indicate that the proposed multi-class cyber-intrusion detection framework outperforms the current conventional and deep learning-based attack detection methods.
{"title":"Attack Detection and Localization in Smart Grid with Image-based Deep Learning","authors":"Mostafa Mohammadpourfard, I. Genc, S. Lakshminarayana, Charalambos Konstantinou","doi":"10.1109/SmartGridComm51999.2021.9631994","DOIUrl":"https://doi.org/10.1109/SmartGridComm51999.2021.9631994","url":null,"abstract":"Smart grid's objective is to enable electricity and information to flow two-way while providing effective, robust, computerized, and decentralized energy delivery. This necessitates the use of state estimation-based techniques and real-time analysis to ensure that effective controls are deployed properly. However, the reliance on communication technologies makes such systems susceptible to sophisticated data integrity attacks imposing serious threats to the overall reliability of smart grid. To detect such attacks, advanced and efficient anomaly detection solutions are needed. In this paper, a two-stage deep learning-based framework is carefully designed by embedding power system's characteristics enabling precise attack detection and localization. First, we encode temporal correlations of the multivariate power system time-series measurements as 2D images using image-based representation approaches such as Gramian Angular Field (GAF) and Recurrence Plot (RP) to obtain the latent data characteristics. These images are then utilized to build a highly reliable and resilient deep Convolutional Neural Network (CNN)-based multi-label classifier capable of learning both low and high level characteristics in the images to detect and discover the exact attack locations without leveraging any prior statistical assumptions. The proposed method is evaluated on the IEEE 57-bus system using real-world load data. Also, a comparative study is carried out. Numerical results indicate that the proposed multi-class cyber-intrusion detection framework outperforms the current conventional and deep learning-based attack detection methods.","PeriodicalId":378884,"journal":{"name":"2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133213837","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 : 2021-09-24DOI: 10.36227/techrxiv.16644652
Arwa Alromih, John A. Clark, P. Gope
Data driven approaches have been widely employed in recent years to detect electricity thefts. Although many techniques have been proposed in the literature, they mainly focus on electricity thefts by consumers of power from the grid. Existing studies do not consider electricity thefts by prosumers, who act as both supplier and consumer in the energy system. This is of great importance as inaccurate reports of prosumers' behaviours can disturb power system operation. Here, the paper examines the role prosumers may play in subverting the energy system and propose a novel means of detecting such malfeasance. Specifically, this work introduces a new electricity theft attack scenarios called balance attacks, where an attacker concurrently modifies his readings along with neighbouring meters in an attempt to balance the total aggregated reading. Such attacks can be difficult to detect by existing solutions that reach detection decisions based on aggregated readings. A novel electricity theft detector is proposed that can detect thefts in the presence of prosumers. Current approaches use either a single model for all users across the system or else a model for each user. Here, a half-way house approach is adopted where a cluster-based detection model is used. In each cluster, the power time series for a user is decomposed into trend, cyclical and residual components. Residual data, along with different features from multiple data sources, are fed in an ML classification algorithm to detect anomalous readings. Simulations have been conducted using a newly generated dataset and results have shown that the proposed model can detect electricity theft with high detection and low error rates. The results also shows that the proposed model can detect thefts with great accuracy from new users.
{"title":"Electricity Theft Detection in the Presence of Prosumers Using a Cluster-based Multi-feature Detection Model","authors":"Arwa Alromih, John A. Clark, P. Gope","doi":"10.36227/techrxiv.16644652","DOIUrl":"https://doi.org/10.36227/techrxiv.16644652","url":null,"abstract":"Data driven approaches have been widely employed in recent years to detect electricity thefts. Although many techniques have been proposed in the literature, they mainly focus on electricity thefts by consumers of power from the grid. Existing studies do not consider electricity thefts by prosumers, who act as both supplier and consumer in the energy system. This is of great importance as inaccurate reports of prosumers' behaviours can disturb power system operation. Here, the paper examines the role prosumers may play in subverting the energy system and propose a novel means of detecting such malfeasance. Specifically, this work introduces a new electricity theft attack scenarios called balance attacks, where an attacker concurrently modifies his readings along with neighbouring meters in an attempt to balance the total aggregated reading. Such attacks can be difficult to detect by existing solutions that reach detection decisions based on aggregated readings. A novel electricity theft detector is proposed that can detect thefts in the presence of prosumers. Current approaches use either a single model for all users across the system or else a model for each user. Here, a half-way house approach is adopted where a cluster-based detection model is used. In each cluster, the power time series for a user is decomposed into trend, cyclical and residual components. Residual data, along with different features from multiple data sources, are fed in an ML classification algorithm to detect anomalous readings. Simulations have been conducted using a newly generated dataset and results have shown that the proposed model can detect electricity theft with high detection and low error rates. The results also shows that the proposed model can detect thefts with great accuracy from new users.","PeriodicalId":378884,"journal":{"name":"2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120957863","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 : 2021-09-15DOI: 10.1109/SmartGridComm51999.2021.9632307
Vivek Deulkar, J. Nair
We consider the problem of optimal charging/discharging of a bank of heterogenous battery units, driven by stochastic electricity generation and demand processes. The batteries in the battery bank may differ with respect to their capacities, ramp constraints, losses, as well as cycling costs. The goal is to minimize the degradation costs associated with battery cycling in the long run; this is posed formally as a Markov decision process. We propose a linear function approximation based Q-learning algorithm for learning the optimal solution, using a specially designed class of kernel functions that approximate the structure of the value functions associated with the MDP. The proposed algorithm is validated via an extensive case study.
{"title":"Optimal Cycling of a Heterogenous Battery Bank via Reinforcement Learning","authors":"Vivek Deulkar, J. Nair","doi":"10.1109/SmartGridComm51999.2021.9632307","DOIUrl":"https://doi.org/10.1109/SmartGridComm51999.2021.9632307","url":null,"abstract":"We consider the problem of optimal charging/discharging of a bank of heterogenous battery units, driven by stochastic electricity generation and demand processes. The batteries in the battery bank may differ with respect to their capacities, ramp constraints, losses, as well as cycling costs. The goal is to minimize the degradation costs associated with battery cycling in the long run; this is posed formally as a Markov decision process. We propose a linear function approximation based Q-learning algorithm for learning the optimal solution, using a specially designed class of kernel functions that approximate the structure of the value functions associated with the MDP. The proposed algorithm is validated via an extensive case study.","PeriodicalId":378884,"journal":{"name":"2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115640433","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 : 2021-09-04DOI: 10.1109/SmartGridComm51999.2021.9632301
Rathinamala Vijay, G. Prasad, Yinjia Huo, Sachin Sm, Prabhakar Tv
We propose a composite diagnostics solution for railway infrastructure monitoring. In particular, we address the issue of soft-fault detection in underground railway cables. We first demonstrate the feasibility of an orthogonal multitone time domain reflectometry based fault detection and location method for railway cabling infrastructure by implementing it using software defined radios. Our practical implementation, comprehensive measurement campaign, and our measurement results guide the design of our overall composite solution. With several diagnostics solutions available in the literature, our conglomerated method presents a technique to consolidate results from multiple diagnostics methods to provide an accurate assessment of underground cable health. We present a Bayesian framework based cable health index computation technique that indicates the extent of degradation that a cable is subject to at any stage during its lifespan. We present the performance results of our proposed solution using real-world measurements to demonstrate its effectiveness.
{"title":"Measurement-based Condition Monitoring of Railway Signaling Cables","authors":"Rathinamala Vijay, G. Prasad, Yinjia Huo, Sachin Sm, Prabhakar Tv","doi":"10.1109/SmartGridComm51999.2021.9632301","DOIUrl":"https://doi.org/10.1109/SmartGridComm51999.2021.9632301","url":null,"abstract":"We propose a composite diagnostics solution for railway infrastructure monitoring. In particular, we address the issue of soft-fault detection in underground railway cables. We first demonstrate the feasibility of an orthogonal multitone time domain reflectometry based fault detection and location method for railway cabling infrastructure by implementing it using software defined radios. Our practical implementation, comprehensive measurement campaign, and our measurement results guide the design of our overall composite solution. With several diagnostics solutions available in the literature, our conglomerated method presents a technique to consolidate results from multiple diagnostics methods to provide an accurate assessment of underground cable health. We present a Bayesian framework based cable health index computation technique that indicates the extent of degradation that a cable is subject to at any stage during its lifespan. We present the performance results of our proposed solution using real-world measurements to demonstrate its effectiveness.","PeriodicalId":378884,"journal":{"name":"2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122430860","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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