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}
Pub Date : 2019-10-01DOI: 10.1109/smartgridcomm.2019.8909737
{"title":"SmartGridComm 2019 Program & Papers","authors":"","doi":"10.1109/smartgridcomm.2019.8909737","DOIUrl":"https://doi.org/10.1109/smartgridcomm.2019.8909737","url":null,"abstract":"","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"85 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":"116827562","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.2019.8909810
Yew Meng Khaw, A. Jahromi, M. Arani, D. Kundur, S. Sanner, Marthe Kassouf
The false tripping of circuit breakers initiated by cyberattacks on protective relays is a growing concern in power systems. This is of high importance because multiple false equipment tripping initiated by coordinated cyberattacks on protective relays can cause large scale disturbance in power systems and potentially lead to cascading failures and blackouts. In this paper, a deep learning based autoencoder is employed to identify anomalous voltage and current data injection to distance protection relays. The autoencoder is first trained with current and voltage data sets representing three-phase faults in zone 1 of a distance relay using a benchmark test system. The autoencoder is then employed to identify anomalies in voltage and current data to prevent false tripping commands by the distance relay. The simulation results verify the capability of the autoencoder model to extract signatures of three-phase faults in the intended zone of a protective relay and detect three-phase fault current and voltage data that do not contain these signatures with high accuracy.
{"title":"Preventing False Tripping Cyberattacks Against Distance Relays: A Deep Learning Approach","authors":"Yew Meng Khaw, A. Jahromi, M. Arani, D. Kundur, S. Sanner, Marthe Kassouf","doi":"10.1109/SmartGridComm.2019.8909810","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909810","url":null,"abstract":"The false tripping of circuit breakers initiated by cyberattacks on protective relays is a growing concern in power systems. This is of high importance because multiple false equipment tripping initiated by coordinated cyberattacks on protective relays can cause large scale disturbance in power systems and potentially lead to cascading failures and blackouts. In this paper, a deep learning based autoencoder is employed to identify anomalous voltage and current data injection to distance protection relays. The autoencoder is first trained with current and voltage data sets representing three-phase faults in zone 1 of a distance relay using a benchmark test system. The autoencoder is then employed to identify anomalies in voltage and current data to prevent false tripping commands by the distance relay. The simulation results verify the capability of the autoencoder model to extract signatures of three-phase faults in the intended zone of a protective relay and detect three-phase fault current and voltage data that do not contain these signatures with high accuracy.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"2 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":"132823490","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.2019.8909698
Abdelrahman Abdelkader, I. Sychev, Riccardo Bonetto, F. Fitzek
In an independent self-sustained micro grid (MG) with limited energy resources, plugged-in electric vehicles (EV) must compete for available excess power supply or demand, modeled as a random variable. This paper proposes a distributed machine learning algorithm based on a Markov decision process (MDP) and non-cooperative game theory, that maximizes the EV’s profit under uncertainty of future MG supply/demand states, while satisfying specific battery constraints imposed by the EV owner. Performance evaluation of the proposed algorithm shows that even with no a priori knowledge of future MG supply/demand states, it achieves average profits of only 43% less than the global optimal profit. Results also show that using a cooperative version of the algorithm leads to a 12% increase in average profits.
{"title":"A Market Oriented, Reinforcement Learning Based Approach for Electric Vehicles Integration in Smart Micro Grids","authors":"Abdelrahman Abdelkader, I. Sychev, Riccardo Bonetto, F. Fitzek","doi":"10.1109/SmartGridComm.2019.8909698","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909698","url":null,"abstract":"In an independent self-sustained micro grid (MG) with limited energy resources, plugged-in electric vehicles (EV) must compete for available excess power supply or demand, modeled as a random variable. This paper proposes a distributed machine learning algorithm based on a Markov decision process (MDP) and non-cooperative game theory, that maximizes the EV’s profit under uncertainty of future MG supply/demand states, while satisfying specific battery constraints imposed by the EV owner. Performance evaluation of the proposed algorithm shows that even with no a priori knowledge of future MG supply/demand states, it achieves average profits of only 43% less than the global optimal profit. Results also show that using a cooperative version of the algorithm leads to a 12% increase in average profits.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"520 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":"123411001","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.2019.8909766
Wenyu Ren, Tuo Yu, Timothy M. Yardley, K. Nahrstedt
The Supervisory Control and Data Acquisition (SCADA) system is the most commonly used industrial control system but is subject to a wide range of serious threats. Intrusion detection systems are deployed to promote the security of SCADA systems, but they continuously generate tremendous number of alerts without further comprehending them. There is a need for an efficient system to correlate alerts and discover attack strategies to provide explainable situational awareness to SCADA operators. In this paper, we present a causal-polytree-based anomaly reasoning framework for SCADA networks, named CAPTAR. CAPTAR takes the meta-alerts from our previous anomaly detection framework EDMAND, correlates the them using a naive Bayes classifier, and matches them to predefined causal polytrees. Utilizing Bayesian inference on the causal polytrees, CAPTAR can produces a high-level view of the security state of the protected SCADA network. Experiments on a prototype of CAPTAR proves its anomaly reasoning ability and its capabilities of satisfying the real-time reasoning requirement.
{"title":"CAPTAR: Causal-Polytree-based Anomaly Reasoning for SCADA Networks","authors":"Wenyu Ren, Tuo Yu, Timothy M. Yardley, K. Nahrstedt","doi":"10.1109/SmartGridComm.2019.8909766","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909766","url":null,"abstract":"The Supervisory Control and Data Acquisition (SCADA) system is the most commonly used industrial control system but is subject to a wide range of serious threats. Intrusion detection systems are deployed to promote the security of SCADA systems, but they continuously generate tremendous number of alerts without further comprehending them. There is a need for an efficient system to correlate alerts and discover attack strategies to provide explainable situational awareness to SCADA operators. In this paper, we present a causal-polytree-based anomaly reasoning framework for SCADA networks, named CAPTAR. CAPTAR takes the meta-alerts from our previous anomaly detection framework EDMAND, correlates the them using a naive Bayes classifier, and matches them to predefined causal polytrees. Utilizing Bayesian inference on the causal polytrees, CAPTAR can produces a high-level view of the security state of the protected SCADA network. Experiments on a prototype of CAPTAR proves its anomaly reasoning ability and its capabilities of satisfying the real-time reasoning requirement.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"36 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":"128279311","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.2019.8909788
Seppo Borenius, J. Costa-Requena, M. Lehtonen, R. Kantola
Smart grids are key enablers for creating sustainable energy systems. On the other hand, they introduce a grid management challenge as power grids become more complex and dynamic. Evolving power grids towards smart grids requires combining electric energy technology with communications and information technology. In a distributed network, time synchronization is often one central question. This paper studies limitations of utilizing the widely used Network Time Protocol (NTP) in current 4G cellular networks for smart grid management. As a specific contribution, an improved, NTP based clock adjustment algorithm is proposed to provide more accurate timing information. Finally, the paper studies capabilities of emerging 5G cellular networks to further improve accuracy of timing provided to user equipment (UE).
智能电网是创建可持续能源系统的关键推动者。另一方面，随着电网变得更加复杂和动态，它们给电网管理带来了挑战。电网向智能电网发展需要将电力能源技术与通信和信息技术相结合。在分布式网络中，时间同步通常是一个中心问题。本文研究了当前4G蜂窝网络中广泛使用的网络时间协议(Network Time Protocol, NTP)在智能电网管理中的局限性。作为一个具体的贡献，提出了一种改进的基于NTP的时钟调整算法，以提供更准确的定时信息。最后，本文研究了新兴5G蜂窝网络的能力，以进一步提高提供给用户设备(UE)的授时精度。
{"title":"Providing Network Time Protocol Based Timing for Smart Grid Measurement and Control Devices in 5G Networks","authors":"Seppo Borenius, J. Costa-Requena, M. Lehtonen, R. Kantola","doi":"10.1109/SmartGridComm.2019.8909788","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909788","url":null,"abstract":"Smart grids are key enablers for creating sustainable energy systems. On the other hand, they introduce a grid management challenge as power grids become more complex and dynamic. Evolving power grids towards smart grids requires combining electric energy technology with communications and information technology. In a distributed network, time synchronization is often one central question. This paper studies limitations of utilizing the widely used Network Time Protocol (NTP) in current 4G cellular networks for smart grid management. As a specific contribution, an improved, NTP based clock adjustment algorithm is proposed to provide more accurate timing information. Finally, the paper studies capabilities of emerging 5G cellular networks to further improve accuracy of timing provided to user equipment (UE).","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":"117223520","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.2019.8909712
Ertem Esiner, D. Mashima, Binbin Chen, Z. Kalbarczyk, D. Nicol
Successful attacks against smart grid systems often exploited the insufficiency of checking mechanisms — e.g., commands are largely executed without checking whether they are issued by the legitimate source and whether they are transmitted through the right network path and hence undergone all necessary mediations and scrutinizes. While adding such enhanced security checking into smart grid systems will significantly raise the bar for attackers, there are two key challenges: 1) the need for real-time, and 2) the need for flexibility — i.e., the scheme needs to be applicable to different deployment settings/communication models and counter various types of attacks. In this work, we design and implement F-Pro, a transparent, bump-in-the-wire solution for fast and flexible message authentication scheme that addresses both challenges. Specifically, by using a lightweight hash-chaining-based scheme that supports provenance verification, F-Pro achieves less than 2 milliseconds end-to-end proving and verifying delay for a single or 2-hop communication in a variety of smart grid communication models, when implemented on a low-cost BeagleBoard-X15 platform.
{"title":"F-Pro: a Fast and Flexible Provenance-Aware Message Authentication Scheme for Smart Grid","authors":"Ertem Esiner, D. Mashima, Binbin Chen, Z. Kalbarczyk, D. Nicol","doi":"10.1109/SmartGridComm.2019.8909712","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909712","url":null,"abstract":"Successful attacks against smart grid systems often exploited the insufficiency of checking mechanisms — e.g., commands are largely executed without checking whether they are issued by the legitimate source and whether they are transmitted through the right network path and hence undergone all necessary mediations and scrutinizes. While adding such enhanced security checking into smart grid systems will significantly raise the bar for attackers, there are two key challenges: 1) the need for real-time, and 2) the need for flexibility — i.e., the scheme needs to be applicable to different deployment settings/communication models and counter various types of attacks. In this work, we design and implement F-Pro, a transparent, bump-in-the-wire solution for fast and flexible message authentication scheme that addresses both challenges. Specifically, by using a lightweight hash-chaining-based scheme that supports provenance verification, F-Pro achieves less than 2 milliseconds end-to-end proving and verifying delay for a single or 2-hop communication in a variety of smart grid communication models, when implemented on a low-cost BeagleBoard-X15 platform.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"50 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":"123634903","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.2019.8909798
Min Zhang, F. Eliassen, Amirhosein Taherkordi, H. Jacobsen, Hwei-Ming Chung, Yan Zhang
Peer-to-peer (P2P) energy trading among neighbouring prosumers is considered as a promising trading method for the future smart grid. Demand response management becomes a critical challenge due to increased penetration of renewable energy. Earlier work mostly considers P2P trading models with only prosumers, while we believe there will still be a role to play for electricity suppliers in local energy markets in the foreseeable future. This paper therefore proposes a trading model in a community based P2P electric energy market that includes local energy suppliers and a community coordinator as market participants, in addition to pure energy consumers, and prosumers. We develop a demand response mechanism for the proposed trading model, in which dynamic pricing for suppliers is used. The community coordinator negotiates with suppliers on the external energy price and trades with them on behalf of the households within the P2P energy market. In our proposed trading model, the behaviour of the suppliers and the community households are modelled as two non-cooperative games. We propose a distributed algorithm to determine the equilibrium of the games. Simulation results show that our model has great effect on reducing the net peak load and increasing the market participants’ profit. Additionally, the proposed mechanism is shown to act as an efficient incentive for pure energy consumers to become prosumers.
{"title":"Energy Trading with Demand Response in a Community-based P2P Energy Market","authors":"Min Zhang, F. Eliassen, Amirhosein Taherkordi, H. Jacobsen, Hwei-Ming Chung, Yan Zhang","doi":"10.1109/SmartGridComm.2019.8909798","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909798","url":null,"abstract":"Peer-to-peer (P2P) energy trading among neighbouring prosumers is considered as a promising trading method for the future smart grid. Demand response management becomes a critical challenge due to increased penetration of renewable energy. Earlier work mostly considers P2P trading models with only prosumers, while we believe there will still be a role to play for electricity suppliers in local energy markets in the foreseeable future. This paper therefore proposes a trading model in a community based P2P electric energy market that includes local energy suppliers and a community coordinator as market participants, in addition to pure energy consumers, and prosumers. We develop a demand response mechanism for the proposed trading model, in which dynamic pricing for suppliers is used. The community coordinator negotiates with suppliers on the external energy price and trades with them on behalf of the households within the P2P energy market. In our proposed trading model, the behaviour of the suppliers and the community households are modelled as two non-cooperative games. We propose a distributed algorithm to determine the equilibrium of the games. Simulation results show that our model has great effect on reducing the net peak load and increasing the market participants’ profit. Additionally, the proposed mechanism is shown to act as an efficient incentive for pure energy consumers to become prosumers.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"35 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":"124063518","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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