Pub Date : 2019-10-01DOI: 10.1109/SmartGridComm.2019.8909694
Xiaoyan He, Chongtao Guo, B. Liao
Vehicular communications, as a key application in five-generation (5G) systems, enable the road traffic to be safer and the road services to be more convenient. Known as vehicle-to-everything (V2X) communications, vehicular communications have differentiated quality of service (QoS) requirements for different kinds of connections. Particularly, vehicle-to-vehicle (V2V) links usually exchange various types of safety-critical information, which may have diverse latency requirements. In this paper, we consider multi-priority queues to ensure strict latency guarantee for the V2V links, which prioritizes different packets for different services. We perform spectrum and power allocation to maximize the ergodic capacity of vehicle-to-network (V2N) links while guaranteeing the V2V links’ latency requirements corresponding to different priority classes. The simulation results demonstrate the validity of the proposed algorithm.
{"title":"Spectrum and Power Allocation for Vehicular Networks with Diverse Latency Requirements","authors":"Xiaoyan He, Chongtao Guo, B. Liao","doi":"10.1109/SmartGridComm.2019.8909694","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909694","url":null,"abstract":"Vehicular communications, as a key application in five-generation (5G) systems, enable the road traffic to be safer and the road services to be more convenient. Known as vehicle-to-everything (V2X) communications, vehicular communications have differentiated quality of service (QoS) requirements for different kinds of connections. Particularly, vehicle-to-vehicle (V2V) links usually exchange various types of safety-critical information, which may have diverse latency requirements. In this paper, we consider multi-priority queues to ensure strict latency guarantee for the V2V links, which prioritizes different packets for different services. We perform spectrum and power allocation to maximize the ergodic capacity of vehicle-to-network (V2N) links while guaranteeing the V2V links’ latency requirements corresponding to different priority classes. The simulation results demonstrate the validity of the proposed algorithm.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"15 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":"122390003","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.2019.8909710
Abdullah Albarakati, Chantal Robillard, Mark Karanfil, Marthe Kassouf, Rachid Hadjidj, M. Debbabi, A. Youssef
Network and system management (NSM) plays an important role in ensuring end-to-end security of power systems. As defined in IEC 62351-7, NSM provides system security awareness through the collection of a large amount of data in order to monitor the power grid operational environments. In this paper, we follow the IEC 62351-7 guidelines to develop an NSM platform for IEC 61850 substations. Then, on top of the developed platform, we build a hybrid, deep learning and rule-based, anomaly detection system. Furthermore, considering IEC 61850 protocols, we develop a list of potential cyber attacks on the substation that are likely to impact the power grid availability. The effectiveness of the proposed anomaly detection system against the identified attacks is confirmed by testing it on an IEEE 8-Bus system in the presence of NSM using a smart grid testbed.1The research reported in this article has been supported by the NSERC/Hydro-Qubec Thales Senior Industrial Research Chair in Smart Grid Security
{"title":"Security Monitoring of IEC 61850 Substations Using IEC 62351-7 Network and System Management1","authors":"Abdullah Albarakati, Chantal Robillard, Mark Karanfil, Marthe Kassouf, Rachid Hadjidj, M. Debbabi, A. Youssef","doi":"10.1109/SmartGridComm.2019.8909710","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909710","url":null,"abstract":"Network and system management (NSM) plays an important role in ensuring end-to-end security of power systems. As defined in IEC 62351-7, NSM provides system security awareness through the collection of a large amount of data in order to monitor the power grid operational environments. In this paper, we follow the IEC 62351-7 guidelines to develop an NSM platform for IEC 61850 substations. Then, on top of the developed platform, we build a hybrid, deep learning and rule-based, anomaly detection system. Furthermore, considering IEC 61850 protocols, we develop a list of potential cyber attacks on the substation that are likely to impact the power grid availability. The effectiveness of the proposed anomaly detection system against the identified attacks is confirmed by testing it on an IEEE 8-Bus system in the presence of NSM using a smart grid testbed.1The research reported in this article has been supported by the NSERC/Hydro-Qubec Thales Senior Industrial Research Chair in Smart Grid Security","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"162 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":"123168834","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.2019.8909745
Li Ma, Kaiming Liu, Yuquan Zou, Yu Li
As both information technology and renewable energy technology continue to develop and rapid reforms sweep across the power industry, the comprehensive energy service is increasingly important in realizing improved energy efficiency and reduced energy costs. The comprehensive energy service uses Power Line Communication (PLC) technology to fully utilize existing power lines for fast and low-cost deployment of various Internet of Things (IoT) terminals used for energy efficiency collection, lighting control, air conditioner control, power distribution monitoring, and more. This document analyzes the feasibility of multi-service transmission based on IPv6 PLC technology. It explores the service characteristics of the comprehensive energy service and the bandwidth and reliability requirements of various services on the communication network. Such services include energy consumption data collection, power distribution monitoring, intelligent lighting control, air conditioner control, power quality analysis, and demand response control in the comprehensive energy service scenario. The feasibility of the technical scheme is verified through theoretical analysis and test.
{"title":"Comprehensive Energy Service - IPv6-based PLC-IoT Technology Enables Converged Multi-Service Transmission","authors":"Li Ma, Kaiming Liu, Yuquan Zou, Yu Li","doi":"10.1109/SmartGridComm.2019.8909745","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909745","url":null,"abstract":"As both information technology and renewable energy technology continue to develop and rapid reforms sweep across the power industry, the comprehensive energy service is increasingly important in realizing improved energy efficiency and reduced energy costs. The comprehensive energy service uses Power Line Communication (PLC) technology to fully utilize existing power lines for fast and low-cost deployment of various Internet of Things (IoT) terminals used for energy efficiency collection, lighting control, air conditioner control, power distribution monitoring, and more. This document analyzes the feasibility of multi-service transmission based on IPv6 PLC technology. It explores the service characteristics of the comprehensive energy service and the bandwidth and reliability requirements of various services on the communication network. Such services include energy consumption data collection, power distribution monitoring, intelligent lighting control, air conditioner control, power quality analysis, and demand response control in the comprehensive energy service scenario. The feasibility of the technical scheme is verified through theoretical analysis and test.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"126 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":"123232059","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.2019.8909717
Diego Kiedanski, D. Kofman, A. Orda, José Horta, Á. Otero
Irrigation in agriculture is a major source of electricity demand flexibility that goes largely unexploited. In this paper we provide a model and a solution to the problem of scheduling irrigation time to minimize electricity costs while satisfying crop water requirements. We propose to apply rebates (aimed to consume renewable energy surplus) that were traditionally offered to the industrial sector, in the agricultural one. Furthermore, an architecture is proposed to overcome some of the limitations that can hinder the adoption of such rebates. The architecture integrates scheduling techniques that have been well studied in the networking literature.Numerical analysis is performed to validate our model and evaluate the proposed scheduling mechanisms, based on real data from a soybean producer and from the corresponding electricity operator. Results indicate that significant cost reductions can be obtained, in particular if the rebates are considered.
{"title":"Exploiting Flexibility in Irrigation While Maintaining Optimal Crop Productivity","authors":"Diego Kiedanski, D. Kofman, A. Orda, José Horta, Á. Otero","doi":"10.1109/SmartGridComm.2019.8909717","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909717","url":null,"abstract":"Irrigation in agriculture is a major source of electricity demand flexibility that goes largely unexploited. In this paper we provide a model and a solution to the problem of scheduling irrigation time to minimize electricity costs while satisfying crop water requirements. We propose to apply rebates (aimed to consume renewable energy surplus) that were traditionally offered to the industrial sector, in the agricultural one. Furthermore, an architecture is proposed to overcome some of the limitations that can hinder the adoption of such rebates. The architecture integrates scheduling techniques that have been well studied in the networking literature.Numerical analysis is performed to validate our model and evaluate the proposed scheduling mechanisms, based on real data from a soybean producer and from the corresponding electricity operator. Results indicate that significant cost reductions can be obtained, in particular if the rebates are considered.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"24 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":"126740242","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.2019.8909746
Mohammad Ekramul Kabir, C. Assi, H. Alameddine, J. Antoun, Jun Yan
The rising awareness for maintaining a clean environment, reducing pollutant emissions, breaking dependencies on oil, as well as tapping into cleaner sources of energies and the remarkable initiatives taken by many countries are nurturing the enormous potential of Electric Vehicles (EV) of being our principal mode of transportation. EVs acceptance, however, is hindered by several challenges, among them is their shorter driving range, slower charging rate, and the lack of ubiquitous availability of charging locations, collectively contributing to higher anxieties for EVs drivers. Meanwhile, the expected immense EV load onto the power distribution sector may compromise the power quality. In this paper, we present a two stage solution to provision and dimension a DC fast charging network that minimizes the deployment cost while ensuring a certain quality of experience for charging (e.g., acceptable waiting time, shorter travel distance to charge, etc.). Further, we pay particular attention to maintain the voltage stability by adding a minimum number of voltage stabilizers upon the need to the power distribution network. We propose, evaluate and compare two CS (charging station) network expansion models to determine a cost effective and adaptive CSs provisioning solution that can efficiently expand the CS network to accommodate future charging demands. Finally, a custom built PYTHON-based discrete event simulator is developed to test our outcomes.
{"title":"Demand Aware Deployment and Expansion Method for an Electric Vehicles Fast Charging Network","authors":"Mohammad Ekramul Kabir, C. Assi, H. Alameddine, J. Antoun, Jun Yan","doi":"10.1109/SmartGridComm.2019.8909746","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909746","url":null,"abstract":"The rising awareness for maintaining a clean environment, reducing pollutant emissions, breaking dependencies on oil, as well as tapping into cleaner sources of energies and the remarkable initiatives taken by many countries are nurturing the enormous potential of Electric Vehicles (EV) of being our principal mode of transportation. EVs acceptance, however, is hindered by several challenges, among them is their shorter driving range, slower charging rate, and the lack of ubiquitous availability of charging locations, collectively contributing to higher anxieties for EVs drivers. Meanwhile, the expected immense EV load onto the power distribution sector may compromise the power quality. In this paper, we present a two stage solution to provision and dimension a DC fast charging network that minimizes the deployment cost while ensuring a certain quality of experience for charging (e.g., acceptable waiting time, shorter travel distance to charge, etc.). Further, we pay particular attention to maintain the voltage stability by adding a minimum number of voltage stabilizers upon the need to the power distribution network. We propose, evaluate and compare two CS (charging station) network expansion models to determine a cost effective and adaptive CSs provisioning solution that can efficiently expand the CS network to accommodate future charging demands. Finally, a custom built PYTHON-based discrete event simulator is developed to test our outcomes.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"13 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":"128400708","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.2019.8909789
Xiang Xie, A. Parlikad, R. S. Puri
Over the past few years, deep learning (DL) based electricity demand forecasting has received considerable attention amongst mathematicians, engineers and data scientists working within the smart grid domain. To this end, deep learning architectures such as deep neural networks (DNN), deep belief networks (DBN) and recurrent neural networks (RNN) have been successfully applied to forecast the generation and consumption of a wide range of energy vectors. In this work, we show preliminary results for a residential load demand forecasting solution which is realized within the framework of power grid digital twin. To this end, a novel class of deep neural networks is adopted wherein the output of the network is efficiently computed via a black-box ordinary differential equation (ODE) solver. We introduce the readers to the main concepts behind this method followed by a real-world, data driven computational benchmark test case designed to study the numerical effectiveness of the proposed approach. Initial results suggest that the ODE based solutions yield acceptable levels of accuracy for wide range of prediction horizons. We conclude that the method could prove as a valuable tool to develop forecasting models within an electrical digital twin (EDT) framework, where, in addition to accurate prediction models, a time horizon independent, computationally scalable and compact model is often desired.
{"title":"A Neural Ordinary Differential Equations Based Approach for Demand Forecasting within Power Grid Digital Twins","authors":"Xiang Xie, A. Parlikad, R. S. Puri","doi":"10.1109/SmartGridComm.2019.8909789","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909789","url":null,"abstract":"Over the past few years, deep learning (DL) based electricity demand forecasting has received considerable attention amongst mathematicians, engineers and data scientists working within the smart grid domain. To this end, deep learning architectures such as deep neural networks (DNN), deep belief networks (DBN) and recurrent neural networks (RNN) have been successfully applied to forecast the generation and consumption of a wide range of energy vectors. In this work, we show preliminary results for a residential load demand forecasting solution which is realized within the framework of power grid digital twin. To this end, a novel class of deep neural networks is adopted wherein the output of the network is efficiently computed via a black-box ordinary differential equation (ODE) solver. We introduce the readers to the main concepts behind this method followed by a real-world, data driven computational benchmark test case designed to study the numerical effectiveness of the proposed approach. Initial results suggest that the ODE based solutions yield acceptable levels of accuracy for wide range of prediction horizons. We conclude that the method could prove as a valuable tool to develop forecasting models within an electrical digital twin (EDT) framework, where, in addition to accurate prediction models, a time horizon independent, computationally scalable and compact model is often desired.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"4 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":"127027920","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.2019.8909797
Yue Yu, Tiecheng Song, Chunxia Su, Xiao Tang, Zhu Han
In this paper, we investigate the electric vehicle (EV) public charging market and especially focus on the time-based pricing mechanism for heterogeneous charging stations (CSs) and the CS allocation mechanism for EVs. As such, we develop a hierarchical game to analyze the interaction between the CSs and the EVs and then formulate it as an equilibrium problem with equilibrium constraints (EPEC). In the proposed game, the CSs act as the leaders who set the charging price at the upper layer and the EVs act as the followers who determine the charging behaviors at the lower layer. At the lower layer, we propose a matching game framework for the CS allocation. At the upper layer, we adopt an algorithm based on the block coordinate descent (BCD) method to solve the pricing problem for CSs. Simulation results show that our proposed framework significantly increases the revenue for the CSs and the utility for the EVs.
{"title":"Hierarchical Game for Electric Vehicle Public Charging Market","authors":"Yue Yu, Tiecheng Song, Chunxia Su, Xiao Tang, Zhu Han","doi":"10.1109/SmartGridComm.2019.8909797","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909797","url":null,"abstract":"In this paper, we investigate the electric vehicle (EV) public charging market and especially focus on the time-based pricing mechanism for heterogeneous charging stations (CSs) and the CS allocation mechanism for EVs. As such, we develop a hierarchical game to analyze the interaction between the CSs and the EVs and then formulate it as an equilibrium problem with equilibrium constraints (EPEC). In the proposed game, the CSs act as the leaders who set the charging price at the upper layer and the EVs act as the followers who determine the charging behaviors at the lower layer. At the lower layer, we propose a matching game framework for the CS allocation. At the upper layer, we adopt an algorithm based on the block coordinate descent (BCD) method to solve the pricing problem for CSs. Simulation results show that our proposed framework significantly increases the revenue for the CSs and the utility for the EVs.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"28 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":"115230979","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.2019.8909777
Yuanqi Gao, Jie Shi, Wei Wang, N. Yu
Dynamic distribution network reconfiguration (DNR) algorithms perform hourly dynamic status changes of sectionalizing and tie switches to reduce network line losses, minimize loss of load, or increase hosting capacity for distributed energy resources. Existing algorithms in this field have demonstrated good results when network parameters are assumed to be known. However, in practice inaccurate distribution network parameter estimates are prevalent. This paper solves the minimum loss dynamic DNR problem without the network parameter information. We formulate the DNR problem as a Markov decision process problem and train an off-policy reinforcement learning (RL) algorithm based on historical operation data set. In the online execution phase, the trained RL agent determines the best network configuration at any time step to minimize the expected total operational cost over the planning horizon, which includes the switching costs. To improve the RL algorithm’s performance, we propose a novel data augmentation method to create additional synthetic training data based on the existing data set. We validate the proposed framework on a 16-bus distribution test feeder with synthetic data. The learned control policy not only reduces the network loss but also improves the voltage profile.
{"title":"Dynamic Distribution Network Reconfiguration Using Reinforcement Learning","authors":"Yuanqi Gao, Jie Shi, Wei Wang, N. Yu","doi":"10.1109/SmartGridComm.2019.8909777","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909777","url":null,"abstract":"Dynamic distribution network reconfiguration (DNR) algorithms perform hourly dynamic status changes of sectionalizing and tie switches to reduce network line losses, minimize loss of load, or increase hosting capacity for distributed energy resources. Existing algorithms in this field have demonstrated good results when network parameters are assumed to be known. However, in practice inaccurate distribution network parameter estimates are prevalent. This paper solves the minimum loss dynamic DNR problem without the network parameter information. We formulate the DNR problem as a Markov decision process problem and train an off-policy reinforcement learning (RL) algorithm based on historical operation data set. In the online execution phase, the trained RL agent determines the best network configuration at any time step to minimize the expected total operational cost over the planning horizon, which includes the switching costs. To improve the RL algorithm’s performance, we propose a novel data augmentation method to create additional synthetic training data based on the existing data set. We validate the proposed framework on a 16-bus distribution test feeder with synthetic data. The learned control policy not only reduces the network loss but also improves the voltage profile.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"71 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":"115987229","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.2019.8909691
A. S. Bansal, David E. Irwin
Distributed solar generation is rising rapidly due to a continuing decline in the cost of solar modules. Nearly all of this solar generation feeds into the grid, since battery based energy storage is expensive to install and maintain. Unfortunately, accommodating unlimited intermittent solar power is challenging, since the grid must continuously balance supply and demand. Thus, governments and public utility commissions are increasingly limiting grid connections of new solar installations. These limitations are likely to become more restrictive over time in many areas as solar disrupts the utility business model. Thus, to employ solar without restrictions, users may increasingly need to defect from the grid. Unfortunately, batteries alone are unlikely to become cost-efficient at enabling grid defection for the foreseeable future. To address the problem, we explore using a mixture of solar, batteries, and a whole-home natural gas generator to shift users partially or entirely off the electric grid. We assess the feasibility and compare the cost and carbon emissions of such an approach with using grid power, as well as existing “net metered” solar installations. Our results show that the approach is trending towards cost-competitive based on current prices, reduces carbon emissions relative to using grid power, and enables users to install solar without restriction.
{"title":"On the Feasibility, Cost, and Carbon Emissions of Grid Defection","authors":"A. S. Bansal, David E. Irwin","doi":"10.1109/SmartGridComm.2019.8909691","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909691","url":null,"abstract":"Distributed solar generation is rising rapidly due to a continuing decline in the cost of solar modules. Nearly all of this solar generation feeds into the grid, since battery based energy storage is expensive to install and maintain. Unfortunately, accommodating unlimited intermittent solar power is challenging, since the grid must continuously balance supply and demand. Thus, governments and public utility commissions are increasingly limiting grid connections of new solar installations. These limitations are likely to become more restrictive over time in many areas as solar disrupts the utility business model. Thus, to employ solar without restrictions, users may increasingly need to defect from the grid. Unfortunately, batteries alone are unlikely to become cost-efficient at enabling grid defection for the foreseeable future. To address the problem, we explore using a mixture of solar, batteries, and a whole-home natural gas generator to shift users partially or entirely off the electric grid. We assess the feasibility and compare the cost and carbon emissions of such an approach with using grid power, as well as existing “net metered” solar installations. Our results show that the approach is trending towards cost-competitive based on current prices, reduces carbon emissions relative to using grid power, and enables users to install solar without restriction.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"191 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":"116138235","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.2019.8909724
Torsten Reissland, Matthias Kuba, J. Robert, A. Koelpin, R. Weigel, F. Lurz
This paper presents several improvements of the energy-pattern based sequence detection (EPSD) algorithm for FSK-based single-phase power line communication (PLC) systems, in terms of complexity, reliability and synchronization. A time synchronization is presented which fulfills the well known task of synchronizing transmitter and receiver, but also helps to avoid transmissions in periods of rough noise conditions. The synchronization method is based on a maximum-likelihood approach that makes use of the phase of the mains voltage. Further improvements concern the codes used for the trans-mitted sequences as well as the combination of the information within both FSK carrier-frequencies in terms of equal gain and maximum ratio combining. Additionally an approach for a low-complexity frame synchronization is presented.
{"title":"Synchronization Approaches and Improvements for a Low-Complexity Power Line Communication System","authors":"Torsten Reissland, Matthias Kuba, J. Robert, A. Koelpin, R. Weigel, F. Lurz","doi":"10.1109/SmartGridComm.2019.8909724","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909724","url":null,"abstract":"This paper presents several improvements of the energy-pattern based sequence detection (EPSD) algorithm for FSK-based single-phase power line communication (PLC) systems, in terms of complexity, reliability and synchronization. A time synchronization is presented which fulfills the well known task of synchronizing transmitter and receiver, but also helps to avoid transmissions in periods of rough noise conditions. The synchronization method is based on a maximum-likelihood approach that makes use of the phase of the mains voltage. Further improvements concern the codes used for the trans-mitted sequences as well as the combination of the information within both FSK carrier-frequencies in terms of equal gain and maximum ratio combining. Additionally an approach for a low-complexity frame synchronization is presented.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"218 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":"122791651","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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