Pub Date : 2019-10-01DOI: 10.1109/SmartGridComm.2019.8909818
O. Duman, Mohsen Ghafouri, Marthe Kassouf, Ribal Atallah, Lingyu Wang, M. Debbabi
Supply chain attacks, which exploit vulnerabilities deliberately injected into devices either before their shipment or through subsequent firmware updates, represent one of the most insidious security threats in smart grids. The deliberate nature of such vulnerabilities means that they can be more difficult to mitigate, e.g., the attack could be designed to autonomously launch from the inside or to cause invisible physical damages to devices over a long time span. Furthermore, they can result in more severe consequences, e.g., the attack could leak sensitive information like crypto keys, or cause a large scale blackout through coordinated devices from the same malicious or hijacked vendor. In this paper, we take the first step towards a better understanding of the threat of supply chain attacks in IEC 61850 substations. Specifically, we first discuss the general concept and unique aspects of supply chain attacks. We then present concrete models of different supply chain attacks through extending the attack graph model and designing a security metric, namely k-Supply. Lastly, we apply such models to quantitatively study the potential impact of supply chain attacks through simulations.
{"title":"Modeling Supply Chain Attacks in IEC 61850 Substations","authors":"O. Duman, Mohsen Ghafouri, Marthe Kassouf, Ribal Atallah, Lingyu Wang, M. Debbabi","doi":"10.1109/SmartGridComm.2019.8909818","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909818","url":null,"abstract":"Supply chain attacks, which exploit vulnerabilities deliberately injected into devices either before their shipment or through subsequent firmware updates, represent one of the most insidious security threats in smart grids. The deliberate nature of such vulnerabilities means that they can be more difficult to mitigate, e.g., the attack could be designed to autonomously launch from the inside or to cause invisible physical damages to devices over a long time span. Furthermore, they can result in more severe consequences, e.g., the attack could leak sensitive information like crypto keys, or cause a large scale blackout through coordinated devices from the same malicious or hijacked vendor. In this paper, we take the first step towards a better understanding of the threat of supply chain attacks in IEC 61850 substations. Specifically, we first discuss the general concept and unique aspects of supply chain attacks. We then present concrete models of different supply chain attacks through extending the attack graph model and designing a security metric, namely k-Supply. Lastly, we apply such models to quantitatively study the potential impact of supply chain attacks through simulations.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132583000","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 : 2019-10-01DOI: 10.1109/SmartGridComm.2019.8909813
Mohammadhadi Shateri, Francisco Messina, P. Piantanida, F. Labeau
The explosion of data collection has raised serious privacy concerns in users due to the possibility that sharing data may also reveal sensitive information. The main goal of a privacy-preserving mechanism is to prevent a malicious third party from inferring sensitive information while keeping the shared data useful. In this paper, we study this problem in the context of time series data and smart meters (SMs) power consumption measurements in particular. Although Mutual Information (MI) between private and released variables has been used as a common information-theoretic privacy measure, it fails to capture the causal time dependencies present in the power consumption time series data. To overcome this limitation, we introduce the Directed Information (DI) as a more meaningful measure of privacy in the considered setting and propose a novel loss function. The optimization is then performed using an adversarial framework where two Recurrent Neural Networks (RNNs), referred to as the releaser and the adversary, are trained with opposite goals. Our empirical studies on real-world data sets from SMs measurements in the worst-case scenario where an attacker has access to all the training data set used by the releaser, validate the proposed method and show the existing trade-offs between privacy and utility.
{"title":"Deep Directed Information-Based Learning for Privacy-Preserving Smart Meter Data Release","authors":"Mohammadhadi Shateri, Francisco Messina, P. Piantanida, F. Labeau","doi":"10.1109/SmartGridComm.2019.8909813","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909813","url":null,"abstract":"The explosion of data collection has raised serious privacy concerns in users due to the possibility that sharing data may also reveal sensitive information. The main goal of a privacy-preserving mechanism is to prevent a malicious third party from inferring sensitive information while keeping the shared data useful. In this paper, we study this problem in the context of time series data and smart meters (SMs) power consumption measurements in particular. Although Mutual Information (MI) between private and released variables has been used as a common information-theoretic privacy measure, it fails to capture the causal time dependencies present in the power consumption time series data. To overcome this limitation, we introduce the Directed Information (DI) as a more meaningful measure of privacy in the considered setting and propose a novel loss function. The optimization is then performed using an adversarial framework where two Recurrent Neural Networks (RNNs), referred to as the releaser and the adversary, are trained with opposite goals. Our empirical studies on real-world data sets from SMs measurements in the worst-case scenario where an attacker has access to all the training data set used by the releaser, validate the proposed method and show the existing trade-offs between privacy and utility.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124324058","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 : 2019-10-01DOI: 10.1109/SmartGridComm.2019.8909732
Xin Lou, Cuong Tran, David K. Y. Yau, Rui Tan, H. Ng, T. Fu, M. Winslett
The cyber-physical systems (CPSes) rely on computing and control techniques to achieve system safety and reliability. However, recent attacks show that these techniques are vulnerable once the cyber-attackers have bypassed air gaps. The attacks may cause service disruptions or even physical damages. This paper designs the built-in attack characterization scheme for one general type of cyber-attacks in CPS, which we call time delay attack, that delays the transmission of the system control commands. We use the recurrent neural networks in deep learning to estimate the delay values from the input trace. Specifically, to deal with the long time-sequence data, we design the deep learning model using stacked bidirectional long short-term memory (LSTM) units. The proposed approach is tested by using the data generated from a power plant control system. The results show that the LSTM-based deep learning approach can work well based on data traces from three sensor measurements, i.e., temperature, pressure, and power generation, in the power plant control system. Moreover, we show that the proposed approach outperforms the base approach based on k-nearest neighbors.
{"title":"Learning-Based Time Delay Attack Characterization for Cyber-Physical Systems","authors":"Xin Lou, Cuong Tran, David K. Y. Yau, Rui Tan, H. Ng, T. Fu, M. Winslett","doi":"10.1109/SmartGridComm.2019.8909732","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909732","url":null,"abstract":"The cyber-physical systems (CPSes) rely on computing and control techniques to achieve system safety and reliability. However, recent attacks show that these techniques are vulnerable once the cyber-attackers have bypassed air gaps. The attacks may cause service disruptions or even physical damages. This paper designs the built-in attack characterization scheme for one general type of cyber-attacks in CPS, which we call time delay attack, that delays the transmission of the system control commands. We use the recurrent neural networks in deep learning to estimate the delay values from the input trace. Specifically, to deal with the long time-sequence data, we design the deep learning model using stacked bidirectional long short-term memory (LSTM) units. The proposed approach is tested by using the data generated from a power plant control system. The results show that the LSTM-based deep learning approach can work well based on data traces from three sensor measurements, i.e., temperature, pressure, and power generation, in the power plant control system. Moreover, we show that the proposed approach outperforms the base approach based on k-nearest neighbors.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114990522","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 : 2019-10-01DOI: 10.1109/SmartGridComm.2019.8909719
Chuang Liu, Lei Kou, G. Cai, Jia-ning Zhou, Yi-qun Meng, Yunhui Yan
Recently, power electronic converters have been widely used since more renewable energy systems have been interconnected with the power grid, among which three-phase PWM rectifier is one of the most widely used in drives of electrical motors, AC and DC transmission, and other energy conversion fields. Like any other power electronic converter, three-phase PWM rectifier may be affected by various faults like open-circuit faults. Therefore, fault diagnosis is extremely important to reduce the maintenance costs and improve the stability of the system. A novel open-circuit faults diagnosis method is proposed. The fault diagnosis method only requires the three-phase AC currents, and then Concordia transform is used to calculate the slopes of the current trajectories (knowledge-based). After that the data-driven method of random forest algorithm is used to train the fault diagnosis classifier with slopes data. Finally the knowledge-based and data-driven fault diagnosis methods are combined to achieve fault diagnosis and location. Experiments are carried out and the experimental results are presented to validate effectiveness, robustness of the proposed fault diagnosis method. Furthermore, the proposed method is suitable for vast majority of three-phase energy conversion systems.
{"title":"Knowledge-based and Data-driven Approach based Fault Diagnosis for Power-Electronics Energy Conversion System","authors":"Chuang Liu, Lei Kou, G. Cai, Jia-ning Zhou, Yi-qun Meng, Yunhui Yan","doi":"10.1109/SmartGridComm.2019.8909719","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909719","url":null,"abstract":"Recently, power electronic converters have been widely used since more renewable energy systems have been interconnected with the power grid, among which three-phase PWM rectifier is one of the most widely used in drives of electrical motors, AC and DC transmission, and other energy conversion fields. Like any other power electronic converter, three-phase PWM rectifier may be affected by various faults like open-circuit faults. Therefore, fault diagnosis is extremely important to reduce the maintenance costs and improve the stability of the system. A novel open-circuit faults diagnosis method is proposed. The fault diagnosis method only requires the three-phase AC currents, and then Concordia transform is used to calculate the slopes of the current trajectories (knowledge-based). After that the data-driven method of random forest algorithm is used to train the fault diagnosis classifier with slopes data. Finally the knowledge-based and data-driven fault diagnosis methods are combined to achieve fault diagnosis and location. Experiments are carried out and the experimental results are presented to validate effectiveness, robustness of the proposed fault diagnosis method. Furthermore, the proposed method is suitable for vast majority of three-phase energy conversion systems.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128876955","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 : 2019-10-01DOI: 10.1109/SmartGridComm.2019.8909733
Diego Kiedanski, Md Umar Hashmi, A. Bušić, D. Kofman
With the massive deployment of distributed energy resources, there has been an increase in the number of end consumers that own photovoltaic panels and storage systems. The optimal use of such storage when facing Time of Use (ToU) prices is directly related to the quality of the load and generation forecasts as well as the algorithm that controls the battery. The sensitivity of such control to different forecast techniques is studied in this paper. It is shown that good and bad forecasts can result in losses in, particularly bad days. Nevertheless, it is observed that performing Model Predictive Control (MPC) with a simple forecast that is representative of the pasts can be profitable under different price and battery scenarios. We observe that performing MPC at a faster sampling time with a receding optimization horizon makes arbitrage less sensitive to uncertainties in forecasting. We use real data from Pecan Street and ToU price levels with different buying and selling price for the numerical experiments.
{"title":"Sensitivity to Forecast Errors in Energy Storage Arbitrage for Residential Consumers","authors":"Diego Kiedanski, Md Umar Hashmi, A. Bušić, D. Kofman","doi":"10.1109/SmartGridComm.2019.8909733","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909733","url":null,"abstract":"With the massive deployment of distributed energy resources, there has been an increase in the number of end consumers that own photovoltaic panels and storage systems. The optimal use of such storage when facing Time of Use (ToU) prices is directly related to the quality of the load and generation forecasts as well as the algorithm that controls the battery. The sensitivity of such control to different forecast techniques is studied in this paper. It is shown that good and bad forecasts can result in losses in, particularly bad days. Nevertheless, it is observed that performing Model Predictive Control (MPC) with a simple forecast that is representative of the pasts can be profitable under different price and battery scenarios. We observe that performing MPC at a faster sampling time with a receding optimization horizon makes arbitrage less sensitive to uncertainties in forecasting. We use real data from Pecan Street and ToU price levels with different buying and selling price for the numerical experiments.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125385906","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 : 2019-10-01DOI: 10.1109/SmartGridComm.2019.8909735
Riccardo Iacobucci, R. Bruno
Shared autonomous electric vehicles (SAEVs) are being introduced in pilot programs and they are expected to be commercially available by the next decade. In this work, we propose a methodology for the joint optimisation of vehicle charging, vehicle-to-grid (V2G) services and fleet rebalancing in mobility systems using SAEVs. The proposed model is implemented as a cascaded model predictive control (MPC) optimisation framework with two different timescales. The first MPC scheme, called energy layer, abstracts the fleet of SAEVs as an aggregate storage system for the sake of model scalability, and it optimises fleet charging and V2G services to minimise electricity cost over a long timescale (hours). The second MPC scheme, called transport layer, optimises short-term vehicle routing and relocation decisions to minimise customers’ waiting times while taking into account the charging constraints derived from the energy layer. A case study using transport and electricity price data for the city of Tokyo is used to validate the model. Results demonstrate that our approach is computationally scalable and it can be applied to large-scale scenarios. In addition, it allows to significantly reduce charging costs with limited impact on passengers’ waiting times
{"title":"Cascaded Model Predictive Control for Shared Autonomous Electric Vehicles Systems with V2G Capabilities","authors":"Riccardo Iacobucci, R. Bruno","doi":"10.1109/SmartGridComm.2019.8909735","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909735","url":null,"abstract":"Shared autonomous electric vehicles (SAEVs) are being introduced in pilot programs and they are expected to be commercially available by the next decade. In this work, we propose a methodology for the joint optimisation of vehicle charging, vehicle-to-grid (V2G) services and fleet rebalancing in mobility systems using SAEVs. The proposed model is implemented as a cascaded model predictive control (MPC) optimisation framework with two different timescales. The first MPC scheme, called energy layer, abstracts the fleet of SAEVs as an aggregate storage system for the sake of model scalability, and it optimises fleet charging and V2G services to minimise electricity cost over a long timescale (hours). The second MPC scheme, called transport layer, optimises short-term vehicle routing and relocation decisions to minimise customers’ waiting times while taking into account the charging constraints derived from the energy layer. A case study using transport and electricity price data for the city of Tokyo is used to validate the model. Results demonstrate that our approach is computationally scalable and it can be applied to large-scale scenarios. In addition, it allows to significantly reduce charging costs with limited impact on passengers’ waiting times","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"241 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123166617","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 : 2019-10-01DOI: 10.1109/SmartGridComm.2019.8909764
Qiuling Yang, Gang Wang, A. Sadeghi, G. Giannakis, Jian Sun
Frequent and sizeable voltage fluctuations become more pronounced with the increasing penetration of distributed renewable generation, and they considerably challenge distribution grids. Voltage regulation schemes so far have relied on either utility-owned devices (e.g., voltage transformers, and shunt capacitors), or more recently, smart power inverters that come with contemporary distributed generation units (e.g., photovoltaic systems, and wind turbines). Nonetheless, due to the distinct response times of those devices, as well as the discrete on-off commitment of capacitor units, joint control of both types of assets is challenging. In this context, a novel two-timescale voltage regulation scheme is developed here by coupling optimization with reinforcement learning advances. Shunt capacitors are configured on a slow timescale (e.g., daily basis) leveraging a deep reinforcement learning algorithm, while optimal setpoints of the power inverters are computed using a linearized distribution flow model on a fast timescale (e.g., every few seconds or minutes). Numerical experiments using a real-world 47-bus distribution feeder showcase the remarkable performance of the novel scheme.
{"title":"Two-Timescale Voltage Regulation in Distribution Grids Using Deep Reinforcement Learning","authors":"Qiuling Yang, Gang Wang, A. Sadeghi, G. Giannakis, Jian Sun","doi":"10.1109/SmartGridComm.2019.8909764","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909764","url":null,"abstract":"Frequent and sizeable voltage fluctuations become more pronounced with the increasing penetration of distributed renewable generation, and they considerably challenge distribution grids. Voltage regulation schemes so far have relied on either utility-owned devices (e.g., voltage transformers, and shunt capacitors), or more recently, smart power inverters that come with contemporary distributed generation units (e.g., photovoltaic systems, and wind turbines). Nonetheless, due to the distinct response times of those devices, as well as the discrete on-off commitment of capacitor units, joint control of both types of assets is challenging. In this context, a novel two-timescale voltage regulation scheme is developed here by coupling optimization with reinforcement learning advances. Shunt capacitors are configured on a slow timescale (e.g., daily basis) leveraging a deep reinforcement learning algorithm, while optimal setpoints of the power inverters are computed using a linearized distribution flow model on a fast timescale (e.g., every few seconds or minutes). Numerical experiments using a real-world 47-bus distribution feeder showcase the remarkable performance of the novel scheme.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123336543","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 : 2019-10-01DOI: 10.1109/SmartGridComm.2019.8909747
Kamal Shahid, Enrico Schiavone, Domagoj Drenjanac, M. Lyhne, R. Olsen, H. Schwefel
Grid topology information plays an important role in grid observability applications such as fault detection and diagnosis. For these applications, data from customer connections should be processed jointly with measurements from the distribution grid by Distribution System Operator (DSO) systems and also correlated to the LV grid topology. In practical DSO systems, the LV grid topology data is frequently included in their databases and may come from different systems such as Geographical Information System (GIS) or other asset management systems, which store a relevant part of the grid topology in a type-specific format. However, in most cases, the grid topology information is not utilized for grid observability applications due to several challenges such as lack of standard data models, complexities in extracting topology information, incorrect/incomplete topology information, dependence on multiple databases etc. Thus, this paper presents challenges and complexities faced by electrical utilities in extracting/using grid topology information for observability applications. The challenges are demonstrated using topology models from two real medium-sized distribution grid operators, which are currently being used in two different European countries.
{"title":"Handling Incomplete and Erroneous Grid Topology Information for Low Voltage Grid Observability","authors":"Kamal Shahid, Enrico Schiavone, Domagoj Drenjanac, M. Lyhne, R. Olsen, H. Schwefel","doi":"10.1109/SmartGridComm.2019.8909747","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909747","url":null,"abstract":"Grid topology information plays an important role in grid observability applications such as fault detection and diagnosis. For these applications, data from customer connections should be processed jointly with measurements from the distribution grid by Distribution System Operator (DSO) systems and also correlated to the LV grid topology. In practical DSO systems, the LV grid topology data is frequently included in their databases and may come from different systems such as Geographical Information System (GIS) or other asset management systems, which store a relevant part of the grid topology in a type-specific format. However, in most cases, the grid topology information is not utilized for grid observability applications due to several challenges such as lack of standard data models, complexities in extracting topology information, incorrect/incomplete topology information, dependence on multiple databases etc. Thus, this paper presents challenges and complexities faced by electrical utilities in extracting/using grid topology information for observability applications. The challenges are demonstrated using topology models from two real medium-sized distribution grid operators, which are currently being used in two different European countries.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121287090","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 : 2019-10-01DOI: 10.1109/SmartGridComm.2019.8909711
Stephan Gross, F. Ponci, A. Monti
Microgrids promise an efficient integration of de-centralized energy resources (DER), mostly fueled by renewable energy sources (RES), and prosumers - pro-active consumers with own generation and storage capacities that can actively manage their load. The arising multi-microgrid concept inter-connects several microgrids in order to increase the operational stability and to raise additional economic benefits for the single microgrid by aggregating spare flexibility from the microgrids and providing it to the utility and energy markets as demand side management (DSM) services. Current research literature discusses both approaches extensively but so far microgrids have not spread widely because of extensive installation costs and operational difficulties. An open software platform based on available standards and control algorithms would decrease these barriers. This paper provides an initial requirement analysis for a multi-/microgrid energy management system (EMS) software platform implementation considering advancements in the telecommunication sector and especially the upgrade to the fifth generation (5G) of the wireless network. At the end of this paper, we present a concept for a scalable test bed for such platform implementations.
{"title":"Multi-Microgrid Energy Management System in Times of 5G","authors":"Stephan Gross, F. Ponci, A. Monti","doi":"10.1109/SmartGridComm.2019.8909711","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909711","url":null,"abstract":"Microgrids promise an efficient integration of de-centralized energy resources (DER), mostly fueled by renewable energy sources (RES), and prosumers - pro-active consumers with own generation and storage capacities that can actively manage their load. The arising multi-microgrid concept inter-connects several microgrids in order to increase the operational stability and to raise additional economic benefits for the single microgrid by aggregating spare flexibility from the microgrids and providing it to the utility and energy markets as demand side management (DSM) services. Current research literature discusses both approaches extensively but so far microgrids have not spread widely because of extensive installation costs and operational difficulties. An open software platform based on available standards and control algorithms would decrease these barriers. This paper provides an initial requirement analysis for a multi-/microgrid energy management system (EMS) software platform implementation considering advancements in the telecommunication sector and especially the upgrade to the fifth generation (5G) of the wireless network. At the end of this paper, we present a concept for a scalable test bed for such platform implementations.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"249 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123030138","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 : 2019-10-01DOI: 10.1109/SmartGridComm.2019.8909744
J. Costa-Requena, Carlos Barjau Estevan, Seppo Borenius
Industrial internet is the main customer for 5G networks. However, mobile networks cannot deliver currently the required reliability and transport infrastructure. In the past mobile networks were designed for personal communications optimized for downlink data transfer. A new transport that provides seamless connectivity between mobile and fixed devices is required. Moreover, reliable timing information has to be delivered to both cellular and fixed devices with predictable delay to enable synchronous communications. This paper studies limitations of utilizing the current transport in mobile networks for smart grid and industrial communications. A new transport layer is proposed and the solution to deliver accurate timing information. Finally, the paper studies capabilities of deploying the proposed transport in both 4G but also in emerging 5G cellular networks.
{"title":"Transport layer and Synchronization for Smart Grid and Industrial Internet in 5G Networks","authors":"J. Costa-Requena, Carlos Barjau Estevan, Seppo Borenius","doi":"10.1109/SmartGridComm.2019.8909744","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909744","url":null,"abstract":"Industrial internet is the main customer for 5G networks. However, mobile networks cannot deliver currently the required reliability and transport infrastructure. In the past mobile networks were designed for personal communications optimized for downlink data transfer. A new transport that provides seamless connectivity between mobile and fixed devices is required. Moreover, reliable timing information has to be delivered to both cellular and fixed devices with predictable delay to enable synchronous communications. This paper studies limitations of utilizing the current transport in mobile networks for smart grid and industrial communications. A new transport layer is proposed and the solution to deliver accurate timing information. Finally, the paper studies capabilities of deploying the proposed transport in both 4G but also in emerging 5G cellular networks.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"428 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121045537","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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