Pub Date : 2021-10-25DOI: 10.1109/SmartGridComm51999.2021.9632316
Zhang Guihai, B. Sikdar
Distributed demand response (DR) is used in smart grids to allow utilities to balance the power supply with the demand by modulating the consumer's behavior by varying the price according to consumption patterns and forecasts. False data injection (FDI) attacks of DR can cause large economical losses for utilities, equipment damage, and issues with power flows. Recently, FDI attack detection methods based on deep learning models have been proposed and these methods have better detection performance as compared to traditional approaches. However, deep learning based models may be vulnerable to adversarial machine learning (AML) attacks. In this paper, we demonstrate the vulnerability of state-of-the-art deep learning based FDI attack detectors in DR scenarios to AML attacks. We propose a new black-box FDI attack framework to fabricate power demands in distributed DR scenarios that is capable of deceiving deep learning based FDI attack detection. The evaluation results show that the proposed AML framework can significantly decrease the FDI detection models accuracy and outperforms other AML techniques proposed in literature.
{"title":"Adversarial Machine Learning Against False Data Injection Attack Detection for Smart Grid Demand Response","authors":"Zhang Guihai, B. Sikdar","doi":"10.1109/SmartGridComm51999.2021.9632316","DOIUrl":"https://doi.org/10.1109/SmartGridComm51999.2021.9632316","url":null,"abstract":"Distributed demand response (DR) is used in smart grids to allow utilities to balance the power supply with the demand by modulating the consumer's behavior by varying the price according to consumption patterns and forecasts. False data injection (FDI) attacks of DR can cause large economical losses for utilities, equipment damage, and issues with power flows. Recently, FDI attack detection methods based on deep learning models have been proposed and these methods have better detection performance as compared to traditional approaches. However, deep learning based models may be vulnerable to adversarial machine learning (AML) attacks. In this paper, we demonstrate the vulnerability of state-of-the-art deep learning based FDI attack detectors in DR scenarios to AML attacks. We propose a new black-box FDI attack framework to fabricate power demands in distributed DR scenarios that is capable of deceiving deep learning based FDI attack detection. The evaluation results show that the proposed AML framework can significantly decrease the FDI detection models accuracy and outperforms other AML techniques proposed in literature.","PeriodicalId":378884,"journal":{"name":"2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121570165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/SmartGridComm51999.2021.9631991
T. Bailey, Jay Johnson, Drew Levin
The sophistication and regularity of power system cybersecurity attacks has been growing in the last decade, leading researchers to investigate new innovative, cyber-resilient tools to help grid operators defend their networks and power systems. One promising approach is to apply recent advances in deep reinforcement learning (DRL) to aid grid operators in making real-time changes to the power system equipment to counteract malicious actions. While multiple transmission studies have been conducted in the past, in this work we investigate the possibility of defending distribution power systems using a DRL agent who has control of a collection of utility-owned distributed energy resources (DER). A game board using a modified version of the IEEE 13-bus model was simulated using OpenDSS to train the DRL agent and compare its performance to a random agent, a greedy agent, and human players. Both the DRL agent and the greedy approach performed well, suggesting a greedy approach can be appropriate for computationally tractable system configurations and a DRL agent is a viable path forward for systems of increased complexity. This work paves the way to create multi-player distribution system control games which could be designed to defend the power grid under a sophisticated cyber-attack.
{"title":"Deep Reinforcement Learning For Online Distribution Power System Cybersecurity Protection","authors":"T. Bailey, Jay Johnson, Drew Levin","doi":"10.1109/SmartGridComm51999.2021.9631991","DOIUrl":"https://doi.org/10.1109/SmartGridComm51999.2021.9631991","url":null,"abstract":"The sophistication and regularity of power system cybersecurity attacks has been growing in the last decade, leading researchers to investigate new innovative, cyber-resilient tools to help grid operators defend their networks and power systems. One promising approach is to apply recent advances in deep reinforcement learning (DRL) to aid grid operators in making real-time changes to the power system equipment to counteract malicious actions. While multiple transmission studies have been conducted in the past, in this work we investigate the possibility of defending distribution power systems using a DRL agent who has control of a collection of utility-owned distributed energy resources (DER). A game board using a modified version of the IEEE 13-bus model was simulated using OpenDSS to train the DRL agent and compare its performance to a random agent, a greedy agent, and human players. Both the DRL agent and the greedy approach performed well, suggesting a greedy approach can be appropriate for computationally tractable system configurations and a DRL agent is a viable path forward for systems of increased complexity. This work paves the way to create multi-player distribution system control games which could be designed to defend the power grid under a sophisticated cyber-attack.","PeriodicalId":378884,"journal":{"name":"2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123398356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/SmartGridComm51999.2021.9632284
Shiyao Zhang, Ka-Cheong Leung
Day-ahead frequency regulation reserves can be procured to compensate power imbalance capacity for the purpose of stabilizing the power system. Due to the intermittency and uncertainty characteristics of renewable generations in a general power system, the dynamic nature of multi-scale system features cannot be fully captured through the existing approaches. This further causes inaccurate prediction and ineffective system operation. To tackle this issue, we propose, in this paper, a deep learning approach to accurately predict the amount of frequency regulation reserves of a general power system through the consideration of network information and power reserves. First, we use the power flow model to generate the net active power imbalance, frequency regulation reserves, and power matrix of a general power system. Second, we combine multiple dynamic system features into a complete input dataset and perform data pre-processing before model training and testing. Third, the proposed deep long short-term memory (DLSTM) model is developed to accurately predict the net active power imbalance in the system, as well as predicting the frequency regulation reserves. Our simulation results show that, when considering the entire power network information, our proposed deep learning approach outperforms the four baseline techniques on predicting the frequency regulation reserves in a general power system. These promising results contribute to large economical benefits in power system operations.
{"title":"Data-Driven Frequency Regulation Reserve Prediction Based on Deep Learning Approach","authors":"Shiyao Zhang, Ka-Cheong Leung","doi":"10.1109/SmartGridComm51999.2021.9632284","DOIUrl":"https://doi.org/10.1109/SmartGridComm51999.2021.9632284","url":null,"abstract":"Day-ahead frequency regulation reserves can be procured to compensate power imbalance capacity for the purpose of stabilizing the power system. Due to the intermittency and uncertainty characteristics of renewable generations in a general power system, the dynamic nature of multi-scale system features cannot be fully captured through the existing approaches. This further causes inaccurate prediction and ineffective system operation. To tackle this issue, we propose, in this paper, a deep learning approach to accurately predict the amount of frequency regulation reserves of a general power system through the consideration of network information and power reserves. First, we use the power flow model to generate the net active power imbalance, frequency regulation reserves, and power matrix of a general power system. Second, we combine multiple dynamic system features into a complete input dataset and perform data pre-processing before model training and testing. Third, the proposed deep long short-term memory (DLSTM) model is developed to accurately predict the net active power imbalance in the system, as well as predicting the frequency regulation reserves. Our simulation results show that, when considering the entire power network information, our proposed deep learning approach outperforms the four baseline techniques on predicting the frequency regulation reserves in a general power system. These promising results contribute to large economical benefits in power system operations.","PeriodicalId":378884,"journal":{"name":"2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"73 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114040116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/SmartGridComm51999.2021.9632313
Muhammad Ramadan Bin Mohamad Saifuddin, David K. Y. Yau, Xin Lou
Under the trend of deregulated Volt/VAR ancillary service market, power distribution grid (PDG) is seeing a growing demand for personally owned distributed energy resources (DERs) installed behind-the-meter as value adding participants. A trustworthy cyber-physical network thus becomes essential for coordinating these decentralized participants (e.g., by aggregators) in supporting Volt/VAR optimisation, a critical conservation voltage reduction (CVR) operation. Meanwhile, oversized inverters, which reserve a larger reactive power (VAR) capacity than needed for real power generation, provide incentive payouts during market participation; they are thus likely to be adopted by future customers. This adoption, as our findings show however, inaugurates a fundamental reliability-security tradeoff, when the surplus VAR capacity, in the wrong hands of cyber attackers, can become a stronger weapon for damaging voltage control as a malicious intent. This paper presents novel analysis of key mechanisms and impacts of a class of data integrity attacks against voltage control during CVR. Evaluation results using a realistic 118-bus test system show that tampering with Volt/VAR control in prosumer-side DER and metering devices, which service D-STATCOM, can cause harmful power quality degradation (e.g., excessive voltage dips) or even power interruption. The results also quantify (i) trade-offs between better Volt/VAR control (i.e., increased reliability) and heightened potency of data integrity attacks (i.e. weakened security) under DER inverter oversizing; and (ii) impacts of these attacks under salient global trends such as increasing DER adoption.
{"title":"Reliability-Security Trade-Off for Distributed Reactive Power Control in Transactive Grid","authors":"Muhammad Ramadan Bin Mohamad Saifuddin, David K. Y. Yau, Xin Lou","doi":"10.1109/SmartGridComm51999.2021.9632313","DOIUrl":"https://doi.org/10.1109/SmartGridComm51999.2021.9632313","url":null,"abstract":"Under the trend of deregulated Volt/VAR ancillary service market, power distribution grid (PDG) is seeing a growing demand for personally owned distributed energy resources (DERs) installed behind-the-meter as value adding participants. A trustworthy cyber-physical network thus becomes essential for coordinating these decentralized participants (e.g., by aggregators) in supporting Volt/VAR optimisation, a critical conservation voltage reduction (CVR) operation. Meanwhile, oversized inverters, which reserve a larger reactive power (VAR) capacity than needed for real power generation, provide incentive payouts during market participation; they are thus likely to be adopted by future customers. This adoption, as our findings show however, inaugurates a fundamental reliability-security tradeoff, when the surplus VAR capacity, in the wrong hands of cyber attackers, can become a stronger weapon for damaging voltage control as a malicious intent. This paper presents novel analysis of key mechanisms and impacts of a class of data integrity attacks against voltage control during CVR. Evaluation results using a realistic 118-bus test system show that tampering with Volt/VAR control in prosumer-side DER and metering devices, which service D-STATCOM, can cause harmful power quality degradation (e.g., excessive voltage dips) or even power interruption. The results also quantify (i) trade-offs between better Volt/VAR control (i.e., increased reliability) and heightened potency of data integrity attacks (i.e. weakened security) under DER inverter oversizing; and (ii) impacts of these attacks under salient global trends such as increasing DER adoption.","PeriodicalId":378884,"journal":{"name":"2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127684572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/SmartGridComm51999.2021.9632299
Pudong Ge, Charalambos Konstantinou, Fei Teng
This paper proposes a cyber-physical cooperative recovery framework to maintain critical power supply, enhancing power systems resilience under extreme events such as earthquakes and hurricanes. Extreme events can possibly damage critical infrastructure in terms of power supply, on both cyber and physical layers. Microgrid (MG) has been widely recognised as the physical-side response to such blackouts, however, the recovery of cyber side is yet fully investigated, especially the cooperatively recovery of cyber-physical power supply. Therefore, a centralised-to-distributed resilient control framework is designed to maintain the power supply of critical loads. In such resilient control, controller-to-controller (C2C) wireless network is utilised to form the emergency distributed communication without a centralised base station. Owing to the limited reliable bandwidth that can be employed in C2C networks, the inevitable delay is considered in designing a discrete control framework, and the corresponding stability criteria are given quantitatively. Finally, the cyber-physical recovery framework is demonstrated effectively through simulations in MATLAB/Simulink.
{"title":"Cyber-Physical Disaster Response of Power Supply Using a Centralised-to-Distributed Framework","authors":"Pudong Ge, Charalambos Konstantinou, Fei Teng","doi":"10.1109/SmartGridComm51999.2021.9632299","DOIUrl":"https://doi.org/10.1109/SmartGridComm51999.2021.9632299","url":null,"abstract":"This paper proposes a cyber-physical cooperative recovery framework to maintain critical power supply, enhancing power systems resilience under extreme events such as earthquakes and hurricanes. Extreme events can possibly damage critical infrastructure in terms of power supply, on both cyber and physical layers. Microgrid (MG) has been widely recognised as the physical-side response to such blackouts, however, the recovery of cyber side is yet fully investigated, especially the cooperatively recovery of cyber-physical power supply. Therefore, a centralised-to-distributed resilient control framework is designed to maintain the power supply of critical loads. In such resilient control, controller-to-controller (C2C) wireless network is utilised to form the emergency distributed communication without a centralised base station. Owing to the limited reliable bandwidth that can be employed in C2C networks, the inevitable delay is considered in designing a discrete control framework, and the corresponding stability criteria are given quantitatively. Finally, the cyber-physical recovery framework is demonstrated effectively through simulations in MATLAB/Simulink.","PeriodicalId":378884,"journal":{"name":"2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127257698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/SmartGridComm51999.2021.9632283
Verena Menzel, J. Hurink, Anne Remke
Within smart grids the safe and dependable distribution of electric power highly depends on the security of Supervisory Control and Data Acquisition (SCADA) systems and their underlying communication protocols. Existing network-based intrusion detection systems for Industrial Control Systems (ICS) are usually centrally applied at the SCADA server and do not take the underlying physical process into account. A recent line of work proposes an additional layer of security via a process-aware approach applied locally at the field stations. This paper broadens the scope of process-aware monitoring by considering the interaction between neighboring field stations, which facilitates upcoming trends of decentralized energy management (DEM). Local security monitoring is lifted to monitoring neighborhoods of field stations, therefore achieving a broader grid coverage w.r.t. security. We provide a distributed monitoring algorithm of the generated sensory readings for this extended setting. The feasibility of the approach is shown via a prototype simulation testbed and a scenario with two subgrids.
{"title":"Securing SCADA networks for smart grids via a distributed evaluation of local sensor data","authors":"Verena Menzel, J. Hurink, Anne Remke","doi":"10.1109/SmartGridComm51999.2021.9632283","DOIUrl":"https://doi.org/10.1109/SmartGridComm51999.2021.9632283","url":null,"abstract":"Within smart grids the safe and dependable distribution of electric power highly depends on the security of Supervisory Control and Data Acquisition (SCADA) systems and their underlying communication protocols. Existing network-based intrusion detection systems for Industrial Control Systems (ICS) are usually centrally applied at the SCADA server and do not take the underlying physical process into account. A recent line of work proposes an additional layer of security via a process-aware approach applied locally at the field stations. This paper broadens the scope of process-aware monitoring by considering the interaction between neighboring field stations, which facilitates upcoming trends of decentralized energy management (DEM). Local security monitoring is lifted to monitoring neighborhoods of field stations, therefore achieving a broader grid coverage w.r.t. security. We provide a distributed monitoring algorithm of the generated sensory readings for this extended setting. The feasibility of the approach is shown via a prototype simulation testbed and a scenario with two subgrids.","PeriodicalId":378884,"journal":{"name":"2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121788292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/SmartGridComm51999.2021.9632332
Magnus Tarle, Mårten Björkman, M. Larsson, L. Nordström, G. Ingeström
Renewable generation is leading to rapidly shifting power flows and it is anticipated that traditional power system control may soon be inadequate to cope with these fluctuations. Traditional control include human-in-the-loop-control schemes while more autonomous control methods can be categorized into Wide-Area Monitoring, Protection and Control systems (WAMPAC). Within this latter group of more advanced systems, reinforcement learning (RL) is a potential candidate to facilitate power system control facing these new challenges. In this paper we demonstrate how a model based reinforcement learning (MBRL) algorithm, which learns and uses an internal model of the world, can be used for autonomous power system control. The proposed RL agent, called the World Model for Autonomous Power System Control (WMAP), includes a safety shield to minimize risk of poor decisions at high uncertainty. The shield can be configured to permit WMAP to take actions with the condition that WMAP asks for guidance, e.g. from a human operator, when in doubt. As an alternative, WMAP could be run in full decision support mode which would require the operator to take all the active decisions. A case study is performed on a IEEE 14-bus system where WMAP is setup to control setpoints of two FACTS devices to emulate grid stability improvements. Results show that improved grid stability is achieved using WMAP while staying within voltage limits. Furthermore, a disastrous situation is avoided when WMAP asks for help in a test scenario event that it had not been trained for.
{"title":"A World Model Based Reinforcement Learning Architecture for Autonomous Power System Control","authors":"Magnus Tarle, Mårten Björkman, M. Larsson, L. Nordström, G. Ingeström","doi":"10.1109/SmartGridComm51999.2021.9632332","DOIUrl":"https://doi.org/10.1109/SmartGridComm51999.2021.9632332","url":null,"abstract":"Renewable generation is leading to rapidly shifting power flows and it is anticipated that traditional power system control may soon be inadequate to cope with these fluctuations. Traditional control include human-in-the-loop-control schemes while more autonomous control methods can be categorized into Wide-Area Monitoring, Protection and Control systems (WAMPAC). Within this latter group of more advanced systems, reinforcement learning (RL) is a potential candidate to facilitate power system control facing these new challenges. In this paper we demonstrate how a model based reinforcement learning (MBRL) algorithm, which learns and uses an internal model of the world, can be used for autonomous power system control. The proposed RL agent, called the World Model for Autonomous Power System Control (WMAP), includes a safety shield to minimize risk of poor decisions at high uncertainty. The shield can be configured to permit WMAP to take actions with the condition that WMAP asks for guidance, e.g. from a human operator, when in doubt. As an alternative, WMAP could be run in full decision support mode which would require the operator to take all the active decisions. A case study is performed on a IEEE 14-bus system where WMAP is setup to control setpoints of two FACTS devices to emulate grid stability improvements. Results show that improved grid stability is achieved using WMAP while staying within voltage limits. Furthermore, a disastrous situation is avoided when WMAP asks for help in a test scenario event that it had not been trained for.","PeriodicalId":378884,"journal":{"name":"2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130560353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/SmartGridComm51999.2021.9632331
I. Shilov, H. L. Cadre, A. Bušić
We consider the interaction between the distribution grid (physical level) managed by the distributed system operator (DSO), and a financial market in which prosumers optimize their demand, generation, and bilateral trades in order to minimize their costs subject to local constraints and bilateral trading reciprocity coupling constraints. We model the interaction problem between the physical and financial levels as a noncooperative generalized Nash equilibrium problem. We compare two designs of the financial level prosumer market: a centralized design and a peer-to-peer fully distributed design. We prove the Pareto efficiency of the equilibria under homogeneity of the trading cost preferences. In addition, we prove that the pricing structure of our noncooperative game does not permit free-lunch behavior. Finally, in the numerical section we provide additional insights on the efficiency loss with respect to the different levels of agents' flexibility and amount of renewables in the network. We also quantify the impact of the prosumers' pricing on the noncooperative game social cost.
{"title":"A Generalized Nash Equilibrium analysis of the interaction between a peer-to-peer financial market and the distribution grid","authors":"I. Shilov, H. L. Cadre, A. Bušić","doi":"10.1109/SmartGridComm51999.2021.9632331","DOIUrl":"https://doi.org/10.1109/SmartGridComm51999.2021.9632331","url":null,"abstract":"We consider the interaction between the distribution grid (physical level) managed by the distributed system operator (DSO), and a financial market in which prosumers optimize their demand, generation, and bilateral trades in order to minimize their costs subject to local constraints and bilateral trading reciprocity coupling constraints. We model the interaction problem between the physical and financial levels as a noncooperative generalized Nash equilibrium problem. We compare two designs of the financial level prosumer market: a centralized design and a peer-to-peer fully distributed design. We prove the Pareto efficiency of the equilibria under homogeneity of the trading cost preferences. In addition, we prove that the pricing structure of our noncooperative game does not permit free-lunch behavior. Finally, in the numerical section we provide additional insights on the efficiency loss with respect to the different levels of agents' flexibility and amount of renewables in the network. We also quantify the impact of the prosumers' pricing on the noncooperative game social cost.","PeriodicalId":378884,"journal":{"name":"2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116598847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/SmartGridComm51999.2021.9631989
Abheek Chatterjee, Hao Huang, K. Davis, A. Layton
The design of resilient power grids is a critical engineering challenge for the smooth functioning of society. Bioinspired design, using a framework called the Ecological Network Analysis (ENA), is a promising solution for improving the resilience of power grids. However, the existing ENA framework can only account or for one type of flow in a network. Thus, the previous applications of ENA in power grid design were limited to the design and evaluation of the power flows only and could not account for the monitoring and control systems and their interactions that are critical to the operation of energy infrastructure. The present work addresses this limitation by proposing a multigraph modeling approach and modified ENA metrics that enable evaluation of the network organization and comparison to biological ecosystems for design inspiration. This work also compares the modeling features of the proposed model and the conventional graphical model of Cyber Physical Power Networks found in the literature to understand the implications of the different modeling approaches.
{"title":"A Multigraph Modeling Approach to Enable Ecological Network Analysis of Cyber Physical Power Networks","authors":"Abheek Chatterjee, Hao Huang, K. Davis, A. Layton","doi":"10.1109/SmartGridComm51999.2021.9631989","DOIUrl":"https://doi.org/10.1109/SmartGridComm51999.2021.9631989","url":null,"abstract":"The design of resilient power grids is a critical engineering challenge for the smooth functioning of society. Bioinspired design, using a framework called the Ecological Network Analysis (ENA), is a promising solution for improving the resilience of power grids. However, the existing ENA framework can only account or for one type of flow in a network. Thus, the previous applications of ENA in power grid design were limited to the design and evaluation of the power flows only and could not account for the monitoring and control systems and their interactions that are critical to the operation of energy infrastructure. The present work addresses this limitation by proposing a multigraph modeling approach and modified ENA metrics that enable evaluation of the network organization and comparison to biological ecosystems for design inspiration. This work also compares the modeling features of the proposed model and the conventional graphical model of Cyber Physical Power Networks found in the literature to understand the implications of the different modeling approaches.","PeriodicalId":378884,"journal":{"name":"2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130046929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/SmartGridComm51999.2021.9631999
F. Lilliu, T. Pedersen, Laurynas Siksnys
To solve the problems caused by the intermittent generation of Renewable Energy Sources, the concept of energy flexibility is of utmost importance, and batteries are devices with high potential in this regard. However, current exact mathematical models specifying battery flexibility cannot scale (exponentially growing runtime) with long time horizons and many batteries. In this paper, we propose to use the FlexOffer (FO) model for this purpose, because: 1) FO is a general model, capturing all types of flexible assets in a unified format and 2) being approximate, it scales very well in terms of number of devices and time horizons. First, we describe the different types of FOs: standard, total-energy constraint and dependency-based (DFOs). Then, we present and discuss FO generation techniques, and provide an analytic method for generating DFOs. Finally, we perform simulations for measuring flexibility in economic terms and time needed for optimization and aggregation. We show that DFOs retain most of the flexibility, while vastly outperforming exact models in optimization and aggregation speed.
{"title":"Capturing Battery Flexibility in a General and Scalable Way Using the FlexOffer Model","authors":"F. Lilliu, T. Pedersen, Laurynas Siksnys","doi":"10.1109/SmartGridComm51999.2021.9631999","DOIUrl":"https://doi.org/10.1109/SmartGridComm51999.2021.9631999","url":null,"abstract":"To solve the problems caused by the intermittent generation of Renewable Energy Sources, the concept of energy flexibility is of utmost importance, and batteries are devices with high potential in this regard. However, current exact mathematical models specifying battery flexibility cannot scale (exponentially growing runtime) with long time horizons and many batteries. In this paper, we propose to use the FlexOffer (FO) model for this purpose, because: 1) FO is a general model, capturing all types of flexible assets in a unified format and 2) being approximate, it scales very well in terms of number of devices and time horizons. First, we describe the different types of FOs: standard, total-energy constraint and dependency-based (DFOs). Then, we present and discuss FO generation techniques, and provide an analytic method for generating DFOs. Finally, we perform simulations for measuring flexibility in economic terms and time needed for optimization and aggregation. We show that DFOs retain most of the flexibility, while vastly outperforming exact models in optimization and aggregation speed.","PeriodicalId":378884,"journal":{"name":"2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130079208","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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