Pub Date : 2019-10-01DOI: 10.1109/SmartGridComm.2019.8909755
Ashfaq Ahmad, J. Khan
We investigate a real-time energy management technique for grid-connected photovoltaic (PV) integrated electric vehicles (EVs) as-service-over vehicular fog. Considering unknown dynamics of system inputs, we employ a virtual queue stability based Lyapunov optimization technique to minimize an average system cost through joint optimization of EV’s PV sufficiency, driving task scheduling delays, energy procurement cost, and EV battery (EVB) management. We obtain all solutions in closed forms which can be easily implemented in real-time EVs asservice-over vehicular fog. Results show that our propositions could achieve a daily EV’s PV sufficiency up to 44.25% and a monthly bill reduction up to 41.80%, while satisfying EV user’s delay and energy requirements.
{"title":"Real-time Energy Management of Solar-integrated Electric Vehicles as-service-over Vehicular Fog","authors":"Ashfaq Ahmad, J. Khan","doi":"10.1109/SmartGridComm.2019.8909755","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909755","url":null,"abstract":"We investigate a real-time energy management technique for grid-connected photovoltaic (PV) integrated electric vehicles (EVs) as-service-over vehicular fog. Considering unknown dynamics of system inputs, we employ a virtual queue stability based Lyapunov optimization technique to minimize an average system cost through joint optimization of EV’s PV sufficiency, driving task scheduling delays, energy procurement cost, and EV battery (EVB) management. We obtain all solutions in closed forms which can be easily implemented in real-time EVs asservice-over vehicular fog. Results show that our propositions could achieve a daily EV’s PV sufficiency up to 44.25% and a monthly bill reduction up to 41.80%, while satisfying EV user’s delay and energy requirements.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"80 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":"128635865","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.2019.8909776
Bijian Dai, Ran Wang, K. Zhu, Jie Hao, Ping Wang
Demand response (DR) is one of the crucial technologies in smart grid for its potential benefits to lower the peak load and smooth the residential demand profiles. The existing DR schemes in the literature mainly focus on optimizing customer’s load profiles but not enough attentions are paid to important factors such as energy consumption patterns of residential appliances, electricity cost, users’ satisfactory level, fairness and energy consumption habits. This paper proposes a flexible DR scheme in smart grid with clustering of residential customers and comprehensively considering the aforementioned factors. New features are extracted from historical data to depict customers’ characteristics and clustering methods are applied to explore their electricity consumption habits. Then such information is further utilized to help schedule the residential appliances in a more flexible but effective manner. Numerical results based on real-world traces demonstrate that the proposed DR scheme performs well in reducing the system expenditure and lowering peak to average ratio (PAR). Our research further analyzes the impacts of various factors, including customers’ preferences and energy consumption patterns, which sheds some illuminations on how to devise efficient DR strategies.
{"title":"A Demand Response Scheme in Smart Grid with Clustering of Residential Customers","authors":"Bijian Dai, Ran Wang, K. Zhu, Jie Hao, Ping Wang","doi":"10.1109/SmartGridComm.2019.8909776","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909776","url":null,"abstract":"Demand response (DR) is one of the crucial technologies in smart grid for its potential benefits to lower the peak load and smooth the residential demand profiles. The existing DR schemes in the literature mainly focus on optimizing customer’s load profiles but not enough attentions are paid to important factors such as energy consumption patterns of residential appliances, electricity cost, users’ satisfactory level, fairness and energy consumption habits. This paper proposes a flexible DR scheme in smart grid with clustering of residential customers and comprehensively considering the aforementioned factors. New features are extracted from historical data to depict customers’ characteristics and clustering methods are applied to explore their electricity consumption habits. Then such information is further utilized to help schedule the residential appliances in a more flexible but effective manner. Numerical results based on real-world traces demonstrate that the proposed DR scheme performs well in reducing the system expenditure and lowering peak to average ratio (PAR). Our research further analyzes the impacts of various factors, including customers’ preferences and energy consumption patterns, which sheds some illuminations on how to devise efficient DR strategies.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"5 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":"134149469","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.2019.8909741
Wei Wang, N. Yu, Jie Shi, Yuanqi Gao
Volt-VAR control (VVC) plays an important role in enhancing energy efficiency, power quality, and reliability of electric power distribution systems by coordinating the operations of equipment such as voltage regulators, on-load tap changers, and capacitor banks. VVC not only keeps voltages in the distribution system within desirable ranges but also reduces system operation costs, which include network losses and equipment depreciation from wear and tear. In this paper, the deep reinforcement learning approach is taken to learn a VVC policy, which minimizes the total operation costs while satisfying the physical operation constraints. The VVC problem is formulated as a constrained Markov decision process and solved by two policy gradient methods, trust region policy optimization and constrained policy optimization. Numerical study results based on IEEE 4-bus and 13-bus distribution test feeders show that the policy gradient methods are capable of learning near-optimal solutions and determining control actions much faster than the optimization-based approaches.
{"title":"Volt-VAR Control in Power Distribution Systems with Deep Reinforcement Learning","authors":"Wei Wang, N. Yu, Jie Shi, Yuanqi Gao","doi":"10.1109/SmartGridComm.2019.8909741","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909741","url":null,"abstract":"Volt-VAR control (VVC) plays an important role in enhancing energy efficiency, power quality, and reliability of electric power distribution systems by coordinating the operations of equipment such as voltage regulators, on-load tap changers, and capacitor banks. VVC not only keeps voltages in the distribution system within desirable ranges but also reduces system operation costs, which include network losses and equipment depreciation from wear and tear. In this paper, the deep reinforcement learning approach is taken to learn a VVC policy, which minimizes the total operation costs while satisfying the physical operation constraints. The VVC problem is formulated as a constrained Markov decision process and solved by two policy gradient methods, trust region policy optimization and constrained policy optimization. Numerical study results based on IEEE 4-bus and 13-bus distribution test feeders show that the policy gradient methods are capable of learning near-optimal solutions and determining control actions much faster than the optimization-based approaches.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"16 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":"116080830","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.2019.8909722
Oluwafemi Kolade, Ling Cheng
An iterative, low-complexity soft-detection (SD) receiver is designed for detecting permutation-aided space-time shift keying (STSK) modulation in an indoor visible light communication (VLC) channel. The indices of the transmitters, which are light emitting diodes (LEDs) are coordinated by antenna matrices using permutations. Therefore, each index is selected only once at each transmit time and transmit block. However, the maximum likelihood (ML) detection becomes complex as the size of the LEDs increase and the STSK scheme requires knowledge of the channel state information (CSI). The proposed SD receiver uses the soft information from the channel to detect the likely transmitted symbols by interpreting the channel output as an assignment problem. The proposed receiver is capable of estimating the likely transmitted symbols with or without the knowledge of the CSI. Results are shown comparing the bit error rate (BER) of the ML receiver with perfect knowledge of the CSI with the SD receiver. The SD receiver matches the ML in cases where higher data rates are required and CSI is known but shows a 5dB loss without the knowledge of the CSI.
{"title":"Permutation-Aided Space-Time Shift Keying for Indoor Visible Light Communication","authors":"Oluwafemi Kolade, Ling Cheng","doi":"10.1109/SmartGridComm.2019.8909722","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909722","url":null,"abstract":"An iterative, low-complexity soft-detection (SD) receiver is designed for detecting permutation-aided space-time shift keying (STSK) modulation in an indoor visible light communication (VLC) channel. The indices of the transmitters, which are light emitting diodes (LEDs) are coordinated by antenna matrices using permutations. Therefore, each index is selected only once at each transmit time and transmit block. However, the maximum likelihood (ML) detection becomes complex as the size of the LEDs increase and the STSK scheme requires knowledge of the channel state information (CSI). The proposed SD receiver uses the soft information from the channel to detect the likely transmitted symbols by interpreting the channel output as an assignment problem. The proposed receiver is capable of estimating the likely transmitted symbols with or without the knowledge of the CSI. Results are shown comparing the bit error rate (BER) of the ML receiver with perfect knowledge of the CSI with the SD receiver. The SD receiver matches the ML in cases where higher data rates are required and CSI is known but shows a 5dB loss without the knowledge of the CSI.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"31 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":"116599853","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.2019.8909699
Moein Sabounchi, Jin Wei
The rapid growth of decentralized energy resources (DERs) has created extensive opportunities to diversify the energy generation portfolio in an exponential rate. At the same time, these opportunities also introduce some essential challenges. One of these challenges is the voltage stability management in a distribution network such as Microgrids. To address this challenge, in this paper, we develop a decentralized voltage management mechanism in the form of an augmented optimal power flow (OPF) problem. In our work, the OPF problem is further decomposed and is solved by exploring the alternating method of multipliers (ADMM). Additionally, reactive power reserves (RPRs) are utilized to generate$/$inject the necessary reactive power at each bus to stabilize the voltage locally and to curb any possible disturbances in the voltage performance. Furthermore, in our work the impact of the communication delay caused by the decentralized management has been considered and mitigated. In this paper, a customized radial 9-bus single feeder distribution network has been used to demonstrate our work. The simulation results have been presented to illustrate the performance of our work.
{"title":"An ADMM-based Decentralized Voltage Management Mechanism for Distribution Networks","authors":"Moein Sabounchi, Jin Wei","doi":"10.1109/SmartGridComm.2019.8909699","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909699","url":null,"abstract":"The rapid growth of decentralized energy resources (DERs) has created extensive opportunities to diversify the energy generation portfolio in an exponential rate. At the same time, these opportunities also introduce some essential challenges. One of these challenges is the voltage stability management in a distribution network such as Microgrids. To address this challenge, in this paper, we develop a decentralized voltage management mechanism in the form of an augmented optimal power flow (OPF) problem. In our work, the OPF problem is further decomposed and is solved by exploring the alternating method of multipliers (ADMM). Additionally, reactive power reserves (RPRs) are utilized to generate$/$inject the necessary reactive power at each bus to stabilize the voltage locally and to curb any possible disturbances in the voltage performance. Furthermore, in our work the impact of the communication delay caused by the decentralized management has been considered and mitigated. In this paper, a customized radial 9-bus single feeder distribution network has been used to demonstrate our work. The simulation results have been presented to illustrate the performance of our work.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"70 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":"121879488","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.2019.8909762
H. Ichikawa, S. Yokogawa, Yuusuke Kawakita, K. Sawada, T. Sogabe, Atsushi Minegishi, H. Uehara
Increasing the proportion of renewable energy— particularly solar energy—in global energy consumption will increase the electricity generated and consumed in the vicinity of consumers thereby limiting the role of centralized power grids to only assure the security of electricity. The inherent features of renewable energy, including the fluctuation in the amount of electricity generated and the time at which electricity can be generated, call for the need of large energy storage systems. Drastic changes would be required not only in the architecture but also in the business models of electricity supply systems. We propose a platform called a “virtual grid system” as a foothold originating from a disruptive innovation strategy to create renewable-energy dominant infrastructures. The system is designed for powering systems comprising IoT devices connected by USB Type-C cables with power delivery protocol (USB-C PD) and for replacing the proliferating primitive off-grid solar systems to provide electricity access to more than 360 million people worldwide. The virtual grid system dynamically creates a power distribution subsystem called a “virtual grid” to be included in an IoT application system. It controls electric flows from power source devices to be synthesized and distributed to load devices in the virtual grid via hub devices called “virtual grid hubs (VG-hub).” A method is developed to control electric flows with concise demand descriptions, and it achieves an optimized flow setting over VG-hub networks using graph theoretic algorithms. A distributed implementation of the virtual grid hub is discussed to increase the distributable power, while the current virtual grid hubs handle too small power for main stream power customers.
增加可再生能源- -特别是太阳能- -在全球能源消费中的比例将增加消费者附近产生和消耗的电力，从而限制集中电网仅保证电力安全的作用。可再生能源的固有特点，包括发电量的波动和可发电的时间，要求需要大型储能系统。不仅需要在架构上，而且需要在电力供应系统的商业模式上进行重大变革。我们提出了一个名为“虚拟电网系统”的平台，作为一个立足点，起源于一项颠覆性创新战略，以创建可再生能源为主的基础设施。该系统旨在为由带有电力传输协议(USB- c PD)的USB Type-C电缆连接的物联网设备组成的系统供电，并用于取代激增的原始离网太阳能系统，为全球超过3.6亿人提供电力。虚拟电网系统动态创建一个称为“虚拟电网”的配电子系统，以包含在物联网应用系统中。它控制来自电源设备的电流，通过称为“虚拟电网集线器(VG-hub)”的集线器设备合成并分配给虚拟电网中的负载设备。提出了一种用简洁的需求描述来控制电流的方法，并利用图论算法实现了VG-hub网络的最优流设置。针对当前虚拟电网集线器处理主流电力用户功率过小的问题，讨论了虚拟电网集线器的分布式实现，以增加可分配功率。
{"title":"An Approach to Renewable-Energy Dominant Grids via Distributed Electrical Energy Platform for IoT Systems","authors":"H. Ichikawa, S. Yokogawa, Yuusuke Kawakita, K. Sawada, T. Sogabe, Atsushi Minegishi, H. Uehara","doi":"10.1109/SmartGridComm.2019.8909762","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909762","url":null,"abstract":"Increasing the proportion of renewable energy— particularly solar energy—in global energy consumption will increase the electricity generated and consumed in the vicinity of consumers thereby limiting the role of centralized power grids to only assure the security of electricity. The inherent features of renewable energy, including the fluctuation in the amount of electricity generated and the time at which electricity can be generated, call for the need of large energy storage systems. Drastic changes would be required not only in the architecture but also in the business models of electricity supply systems. We propose a platform called a “virtual grid system” as a foothold originating from a disruptive innovation strategy to create renewable-energy dominant infrastructures. The system is designed for powering systems comprising IoT devices connected by USB Type-C cables with power delivery protocol (USB-C PD) and for replacing the proliferating primitive off-grid solar systems to provide electricity access to more than 360 million people worldwide. The virtual grid system dynamically creates a power distribution subsystem called a “virtual grid” to be included in an IoT application system. It controls electric flows from power source devices to be synthesized and distributed to load devices in the virtual grid via hub devices called “virtual grid hubs (VG-hub).” A method is developed to control electric flows with concise demand descriptions, and it achieves an optimized flow setting over VG-hub networks using graph theoretic algorithms. A distributed implementation of the virtual grid hub is discussed to increase the distributable power, while the current virtual grid hubs handle too small power for main stream power customers.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"60 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":"121913779","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.2019.8909793
Yongxuan Zhang, Jun Yan
The smart grid faces growing cyber-physical attack threats aimed at the critical systems and processes communicating over the complex cyber-infrastructure. Thanks to the increasing availability of high-quality data and the success of deep learning algorithms, machine learning (ML)-based detection and classification have been increasingly effective and adopted against sophisticated attacks. However, many of these techniques rely on the assumptions that the training and testing datasets share the same distribution and the same class labels in a stationary environment. As such assumptions may fail to hold when the system dynamics shift and new threat variants emerge in a non-stationary environment, the capability of trained ML models to adapt in complex operating scenarios will be critical to their deployment in real-world smart grid communications. To this aim, this paper proposes a domain-adversarial transfer learning framework for robust intrusion detection against smart grid attacks. The framework introduces domain-adversarial training to create a mapping between the labeled source domain and the unlabeled target domain so that the classifiers can learn in a new feature space against unknown threats. The proposed framework with different baseline classifiers was evaluated using a smart grid cyber-attack dataset collected over a realistic hardware-in-the- loop security testbed. The results have demonstrated effective performance improvements of trained classifiers against unseen threats of different types and locations.
{"title":"Domain-Adversarial Transfer Learning for Robust Intrusion Detection in the Smart Grid","authors":"Yongxuan Zhang, Jun Yan","doi":"10.1109/SmartGridComm.2019.8909793","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909793","url":null,"abstract":"The smart grid faces growing cyber-physical attack threats aimed at the critical systems and processes communicating over the complex cyber-infrastructure. Thanks to the increasing availability of high-quality data and the success of deep learning algorithms, machine learning (ML)-based detection and classification have been increasingly effective and adopted against sophisticated attacks. However, many of these techniques rely on the assumptions that the training and testing datasets share the same distribution and the same class labels in a stationary environment. As such assumptions may fail to hold when the system dynamics shift and new threat variants emerge in a non-stationary environment, the capability of trained ML models to adapt in complex operating scenarios will be critical to their deployment in real-world smart grid communications. To this aim, this paper proposes a domain-adversarial transfer learning framework for robust intrusion detection against smart grid attacks. The framework introduces domain-adversarial training to create a mapping between the labeled source domain and the unlabeled target domain so that the classifiers can learn in a new feature space against unknown threats. The proposed framework with different baseline classifiers was evaluated using a smart grid cyber-attack dataset collected over a realistic hardware-in-the- loop security testbed. The results have demonstrated effective performance improvements of trained classifiers against unseen threats of different types and locations.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"8 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":"130170216","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}
Electricity Retailers offer various utility plans in the hope that the increased competition would result in lower prices, improved service, and innovative product offerings. In this paper, we present the retail electric provider’s (REP) optimal pricing strategy for residential customers in smart grid, in which the REP offers multiple utility plans for customers with different needs, which includes a flat-rate plan, a multi-stage plan, and a lump-sum fee plan. The residential customers select the utility plan that maximize their own payoffs by considering their own demands and the pricing strategies of the three plans. In the other way around, the REP optimizes its profit by carefully designing its pricing strategy based on residential customers’ decisions. To obtain insights of such a highly coupled system, we consider a system with one REP and a group of customers in need of electricity. We propose a three-stage Stackelberg game model, in which the REP acts as the leader who decides the specific plans to offer at Stage I, then announces the price for each plan in stage II, and finally the customers act as followers that select plans in stage III. We derive the market equilibrium by analyzing customers’ decisions among the plans under different pricing schemes. Then, we provide the RP’s optimal pricing strategies to maximize its profit. In the end, we give the optimal decisions for REP on the specific plan(s) to offer while considering each customer’s evaluation and demand. Both the analytical and simulation results show that the lump-sum fee plan can maximize RP’s profit in most cases.
{"title":"Optimal Pricing Strategy for Residential Electricity Usage in Smart Grid","authors":"Quan-Hui Liu, Ying Zhou, Zhongtao Yue, Bidushi Barua, Yanru Zhang","doi":"10.1109/SmartGridComm.2019.8909769","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909769","url":null,"abstract":"Electricity Retailers offer various utility plans in the hope that the increased competition would result in lower prices, improved service, and innovative product offerings. In this paper, we present the retail electric provider’s (REP) optimal pricing strategy for residential customers in smart grid, in which the REP offers multiple utility plans for customers with different needs, which includes a flat-rate plan, a multi-stage plan, and a lump-sum fee plan. The residential customers select the utility plan that maximize their own payoffs by considering their own demands and the pricing strategies of the three plans. In the other way around, the REP optimizes its profit by carefully designing its pricing strategy based on residential customers’ decisions. To obtain insights of such a highly coupled system, we consider a system with one REP and a group of customers in need of electricity. We propose a three-stage Stackelberg game model, in which the REP acts as the leader who decides the specific plans to offer at Stage I, then announces the price for each plan in stage II, and finally the customers act as followers that select plans in stage III. We derive the market equilibrium by analyzing customers’ decisions among the plans under different pricing schemes. Then, we provide the RP’s optimal pricing strategies to maximize its profit. In the end, we give the optimal decisions for REP on the specific plan(s) to offer while considering each customer’s evaluation and demand. Both the analytical and simulation results show that the lump-sum fee plan can maximize RP’s profit in most cases.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"3 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":"128790947","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.2019.8909786
Shantanu Chakrabarty, B. Sikdar
On-Load Tap Changing transformers are a widely used voltage regulation device. In the context of modern or smart grids, the control signals, i.e., the tap change commands are sent through SCADA channels. It is well known that the power system SCADA networks are prone to attacks involving injection of false data or commands. While false data injection is well explored in existing literature, attacks involving malicious control signals/commands are relatively unexplored. In this paper, an algorithm is developed to detect a stealthily introduced malicious tap change command through a compromised SCADA channel. This algorithm is based on the observation that a stealthily introduced false data or command masks the true estimation of only a few state variables. This leaves the rest of the state variables to show signs of a change in system state brought about by the attack. Using this observation, an index is formulated based on the ratios of injection or branch currents to voltages of the terminal nodes of the tap changers. This index shows a significant increase when there is a false tap command injection, resulting in easy classification from normal scenarios where there is no attack. The algorithm is computationally light, easy to implement and reliable when tested extensively on several tap changers placed in an IEEE 118-bus system.
{"title":"A Methodology for Detecting Stealthy Transformer Tap Command Injection Attacks in Smart Grids","authors":"Shantanu Chakrabarty, B. Sikdar","doi":"10.1109/SmartGridComm.2019.8909786","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909786","url":null,"abstract":"On-Load Tap Changing transformers are a widely used voltage regulation device. In the context of modern or smart grids, the control signals, i.e., the tap change commands are sent through SCADA channels. It is well known that the power system SCADA networks are prone to attacks involving injection of false data or commands. While false data injection is well explored in existing literature, attacks involving malicious control signals/commands are relatively unexplored. In this paper, an algorithm is developed to detect a stealthily introduced malicious tap change command through a compromised SCADA channel. This algorithm is based on the observation that a stealthily introduced false data or command masks the true estimation of only a few state variables. This leaves the rest of the state variables to show signs of a change in system state brought about by the attack. Using this observation, an index is formulated based on the ratios of injection or branch currents to voltages of the terminal nodes of the tap changers. This index shows a significant increase when there is a false tap command injection, resulting in easy classification from normal scenarios where there is no attack. The algorithm is computationally light, easy to implement and reliable when tested extensively on several tap changers placed in an IEEE 118-bus system.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"68 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":"127711574","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.2019.8909748
Weifeng Zhong, Kan Xie, Yi Liu, Chao Yang, Shengli Xie, Yan Zhang
This paper studies dynamic scheduling of a self-interested battery charging station that provides fully-charged batteries for electric vehicle (EV) battery swapping services. The charging station receives multi-type battery orders from the demand side, and it can refuse the orders or admit part of the orders according to current system states. If admitted, battery orders have to be served completely before predefined deadlines. Based on Lyapunov optimization framework, a dynamic scheduling approach is developed, which allows the charging station to observe real-time system states and make scheduling decisions in an online fashion. In theoretical analysis, the feasibility and suboptimality of the proposed approach are proven. Based on the analysis, the feasible ranges of algorithm parameters are derived, ensuring that battery orders can be completed before deadlines. In simulation, actual real-time electricity data is used. The results show that the proposed approach satisfies the deadline constraints and achieves higher profit than other benchmark approaches.
{"title":"Dynamic Scheduling of Multi-Type Battery Charging Stations for EV Battery Swapping","authors":"Weifeng Zhong, Kan Xie, Yi Liu, Chao Yang, Shengli Xie, Yan Zhang","doi":"10.1109/SmartGridComm.2019.8909748","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2019.8909748","url":null,"abstract":"This paper studies dynamic scheduling of a self-interested battery charging station that provides fully-charged batteries for electric vehicle (EV) battery swapping services. The charging station receives multi-type battery orders from the demand side, and it can refuse the orders or admit part of the orders according to current system states. If admitted, battery orders have to be served completely before predefined deadlines. Based on Lyapunov optimization framework, a dynamic scheduling approach is developed, which allows the charging station to observe real-time system states and make scheduling decisions in an online fashion. In theoretical analysis, the feasibility and suboptimality of the proposed approach are proven. Based on the analysis, the feasible ranges of algorithm parameters are derived, ensuring that battery orders can be completed before deadlines. In simulation, actual real-time electricity data is used. The results show that the proposed approach satisfies the deadline constraints and achieves higher profit than other benchmark approaches.","PeriodicalId":377150,"journal":{"name":"2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"46 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120992485","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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