Pub Date : 2018-10-01DOI: 10.1109/SmartGridComm.2018.8587597
Muhammad Fahim, A. Sillitti
Smart buildings provide an excellent opportunity to monitor the energy consumption behavior. It can assist the building management to find unexpected energy usage patterns. In this research, we present our model to find abnormal energy consumption patterns by analyzing the temporal data streams gathered from smart meters. We investigate support vector regression with radial basis function to find the mismatch between actual and expected energy consumption. It has the ability to map the non-linearity of data and predict expected energy consumption. We build the energy usage profile and provide visualization services over it. Furthermore, energy profiles may be used for different objectives including customer classification and load forecasting. In this preliminary study, we performed the experiments over a real electrical load measurements dataset collected from a dwelling. The obtained results suggest that our proposed model is feasible and practical solution to detect anomalies and provide good insight to visualize the energy consumption behavior.
{"title":"An Anomaly Detection Model for Enhancing Energy Management in Smart Buildings","authors":"Muhammad Fahim, A. Sillitti","doi":"10.1109/SmartGridComm.2018.8587597","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2018.8587597","url":null,"abstract":"Smart buildings provide an excellent opportunity to monitor the energy consumption behavior. It can assist the building management to find unexpected energy usage patterns. In this research, we present our model to find abnormal energy consumption patterns by analyzing the temporal data streams gathered from smart meters. We investigate support vector regression with radial basis function to find the mismatch between actual and expected energy consumption. It has the ability to map the non-linearity of data and predict expected energy consumption. We build the energy usage profile and provide visualization services over it. Furthermore, energy profiles may be used for different objectives including customer classification and load forecasting. In this preliminary study, we performed the experiments over a real electrical load measurements dataset collected from a dwelling. The obtained results suggest that our proposed model is feasible and practical solution to detect anomalies and provide good insight to visualize the energy consumption behavior.","PeriodicalId":213523,"journal":{"name":"2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116838595","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 : 2018-10-01DOI: 10.1109/SmartGridComm.2018.8587524
An Chen, Wenzhan Song, Fangyu Li, J. Mohammadpour
Smart Microgrid (SMG), integrated with renewable energy, energy storage system and advanced bidirectional communication network, has been envisioned to improve efficiency and reliability of power delivery. However, the stochastic nature of renewable energy and privacy concerns due to intensive bidirectional data exchange make the traditional energy management system (EMS) perform poorly. In order to improve operational efficiency and customers’ satisfaction, we propose a distributed cooperative energy management system (DCEMS). We adopt recurrent neural network with long short-term memory to predict the solar energy generation with high accuracy. We then solve the underlying economic dispatch problem with distributed scalable Alternating Direction Method of Multipliers (ADMM) algorithm to avoid single point of failure problem and preserve customers’ privacy. In the first stage, each SMG optimizes its operation decision vector in a centralized manner based on one-day ahead solar energy generation prediction. In the second stage, all SMGs share their energy exchange information with directly connected neighboring SMGs to cooperatively optimize the global operation cost. The proposed DCEMS is deployed in our distributed SMGs emulation platform and its performance is compared with other approaches. The results show that the proposed DCEMS outperforms heuristic rule-based EMS by more than 30%. It can also protect customers’ privacy and avoid single point of failure without degrading performance too much compared to centralized EMS.
{"title":"Distributed Cooperative Energy Management in Smart Microgrids with Solar Energy Prediction","authors":"An Chen, Wenzhan Song, Fangyu Li, J. Mohammadpour","doi":"10.1109/SmartGridComm.2018.8587524","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2018.8587524","url":null,"abstract":"Smart Microgrid (SMG), integrated with renewable energy, energy storage system and advanced bidirectional communication network, has been envisioned to improve efficiency and reliability of power delivery. However, the stochastic nature of renewable energy and privacy concerns due to intensive bidirectional data exchange make the traditional energy management system (EMS) perform poorly. In order to improve operational efficiency and customers’ satisfaction, we propose a distributed cooperative energy management system (DCEMS). We adopt recurrent neural network with long short-term memory to predict the solar energy generation with high accuracy. We then solve the underlying economic dispatch problem with distributed scalable Alternating Direction Method of Multipliers (ADMM) algorithm to avoid single point of failure problem and preserve customers’ privacy. In the first stage, each SMG optimizes its operation decision vector in a centralized manner based on one-day ahead solar energy generation prediction. In the second stage, all SMGs share their energy exchange information with directly connected neighboring SMGs to cooperatively optimize the global operation cost. The proposed DCEMS is deployed in our distributed SMGs emulation platform and its performance is compared with other approaches. The results show that the proposed DCEMS outperforms heuristic rule-based EMS by more than 30%. It can also protect customers’ privacy and avoid single point of failure without degrading performance too much compared to centralized EMS.","PeriodicalId":213523,"journal":{"name":"2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123476114","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 : 2018-10-01DOI: 10.1109/SmartGridComm.2018.8587538
I. Sychev, Oleksandr Zhdanenko, Riccardo Bonetto, F. Fitzek
Accurate software based simulation of (complex) dynamic and, possibly, stochastic systems is a key component of the design and test of control strategies. Simulation tools developed according to software design best practices provide engineers and researchers with efficient and easy to use representations of the world. Hence, allowing for faster control algorithms design and test. Here we present ARIES, a (low voltage) smArt gRid dIscrete Event Simulator meant to enable large scale learning and easy smart grid applications design and testing. ARIES is designed according to object oriented best practices, and it is implemented in Python 3. ARIES is equipped with a REST API to actively interact with the simulations, it features a simulation results storage system based on a MongoDB database, and a event management system based on a redis in-memory data structure store used as message broker.
{"title":"ARIES: Low Voltage smArt gRid dIscrete Event Simulator to Enable Large Scale Learning in the Power Distribution Networks","authors":"I. Sychev, Oleksandr Zhdanenko, Riccardo Bonetto, F. Fitzek","doi":"10.1109/SmartGridComm.2018.8587538","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2018.8587538","url":null,"abstract":"Accurate software based simulation of (complex) dynamic and, possibly, stochastic systems is a key component of the design and test of control strategies. Simulation tools developed according to software design best practices provide engineers and researchers with efficient and easy to use representations of the world. Hence, allowing for faster control algorithms design and test. Here we present ARIES, a (low voltage) smArt gRid dIscrete Event Simulator meant to enable large scale learning and easy smart grid applications design and testing. ARIES is designed according to object oriented best practices, and it is implemented in Python 3. ARIES is equipped with a REST API to actively interact with the simulations, it features a simulation results storage system based on a MongoDB database, and a event management system based on a redis in-memory data structure store used as message broker.","PeriodicalId":213523,"journal":{"name":"2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131005494","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 : 2018-10-01DOI: 10.1109/SmartGridComm.2018.8587416
Toni Mancini, F. Mari, I. Melatti, Ivano Salvo, E. Tronci, J. Gruber, B. Hayes, M. Prodanović, Lars Elmegaard
By using small computing devices deployed at user premises, Autonomous Demand Response (ADR) adapts users electricity consumption to given time-dependent electricity tariffs. This allows end-users to save on their electricity bill and Distribution System Operators to optimise (through suitable time-dependent tariffs) management of the electric grid by avoiding demand peaks. Unfortunately, even with ADR, users power consumption may deviate from the expected (minimum cost) one, e.g., because ADR devices fail to correctly forecast energy needs at user premises. As a result, the aggregated power demand may present undesirable peaks. In this paper we address such a problem by presenting methods and a software tool (APD-Analyser) implementing them, enabling Distribution System Operators to effectively verify that a given time-dependent electricity tariff achieves the desired goals even when end-users deviate from their expected behaviour. We show feasibility of the proposed approach through a realistic scenario from a medium voltage Danish distribution network.
{"title":"Parallel Statistical Model Checking for Safety Verification in Smart Grids","authors":"Toni Mancini, F. Mari, I. Melatti, Ivano Salvo, E. Tronci, J. Gruber, B. Hayes, M. Prodanović, Lars Elmegaard","doi":"10.1109/SmartGridComm.2018.8587416","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2018.8587416","url":null,"abstract":"By using small computing devices deployed at user premises, Autonomous Demand Response (ADR) adapts users electricity consumption to given time-dependent electricity tariffs. This allows end-users to save on their electricity bill and Distribution System Operators to optimise (through suitable time-dependent tariffs) management of the electric grid by avoiding demand peaks. Unfortunately, even with ADR, users power consumption may deviate from the expected (minimum cost) one, e.g., because ADR devices fail to correctly forecast energy needs at user premises. As a result, the aggregated power demand may present undesirable peaks. In this paper we address such a problem by presenting methods and a software tool (APD-Analyser) implementing them, enabling Distribution System Operators to effectively verify that a given time-dependent electricity tariff achieves the desired goals even when end-users deviate from their expected behaviour. We show feasibility of the proposed approach through a realistic scenario from a medium voltage Danish distribution network.","PeriodicalId":213523,"journal":{"name":"2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132232428","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 : 2018-10-01DOI: 10.1109/SmartGridComm.2018.8587539
C. Cheung, Wen Zhong, Chuanxiu Xiong, Ajitesh Srivastava, R. Kannan, V. Prasanna
To facilitate deep penetration of solar energy in smart grids, we need high observability of solar generation at the edges of the grid. Current advanced metering infrastructures (AMI) only monitor the aggregated measurements from net-metered households, but disaggregated consumption and solar generation components are required for grid optimizations. We propose an unsupervised disaggregation model for disaggregating solar generation from AMI measurements without the need of training data. The model requires only AMI measurements from consumers in a region and the solar irradiance as input, and models the consumption of consumers by neighboring households without rooftop photovoltaics (PV) to perform the disaggregation. We evaluate our results on a real life dataset from Austin, Texas. We show that our model is able to disaggregate consumption and solar generation measurements with 42.24% and 31.67% less mean squared error, respectively, in comparison to a baseline technique that uses supervised learning. This shows that our model is capable of disaggregating historical data even if the dataset has no training data and only contains minimal exogenous data.
{"title":"Behind-the-Meter Solar Generation Disaggregation using Consumer Mixture Models","authors":"C. Cheung, Wen Zhong, Chuanxiu Xiong, Ajitesh Srivastava, R. Kannan, V. Prasanna","doi":"10.1109/SmartGridComm.2018.8587539","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2018.8587539","url":null,"abstract":"To facilitate deep penetration of solar energy in smart grids, we need high observability of solar generation at the edges of the grid. Current advanced metering infrastructures (AMI) only monitor the aggregated measurements from net-metered households, but disaggregated consumption and solar generation components are required for grid optimizations. We propose an unsupervised disaggregation model for disaggregating solar generation from AMI measurements without the need of training data. The model requires only AMI measurements from consumers in a region and the solar irradiance as input, and models the consumption of consumers by neighboring households without rooftop photovoltaics (PV) to perform the disaggregation. We evaluate our results on a real life dataset from Austin, Texas. We show that our model is able to disaggregate consumption and solar generation measurements with 42.24% and 31.67% less mean squared error, respectively, in comparison to a baseline technique that uses supervised learning. This shows that our model is capable of disaggregating historical data even if the dataset has no training data and only contains minimal exogenous data.","PeriodicalId":213523,"journal":{"name":"2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114574397","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 : 2018-10-01DOI: 10.1109/SmartGridComm.2018.8587464
Chi Zhang, S. Kuppannagari, R. Kannan, V. Prasanna
The availability of fine grained time series data is a pre-requisite for research in smart-grids. While data for transmission systems is relatively easily obtainable, issues related to data collection, security and privacy hinder the widespread public availability/accessibility of such datasets at the distribution system level. This has prevented the larger research community from effectively applying sophisticated machine learning algorithms to significantly improve the distribution-level accuracy of predictions and increase the efficiency of grid operations. Synthetic dataset generation has proven to be a promising solution for addressing data availability issues in various domains such as computer vision, natural language processing and medicine. However, its exploration in the smart grid context remains unsatisfactory. Previous works have tried to generate synthetic datasets by modeling the underlying system dynamics: an approach which is difficult, time consuming, error prone and often times infeasible in many problems. In this work, we propose a novel data-driven approach to synthetic dataset generation by utilizing deep generative adversarial networks (GAN) to learn the conditional probability distribution of essential features in the real dataset and generate samples based on the learned distribution. To evaluate our synthetically generated dataset, we measure the maximum mean discrepancy (MMD) between real and synthetic datasets as probability distributions, and show that their sampling distance converges. To further validate our synthetic dataset, we perform common smart grid tasks such as k-means clustering and short-term prediction on both datasets. Experimental results show the efficacy of our synthetic dataset approach: the real and synthetic datasets are indistinguishable by solely examining the output of these tasks.
{"title":"Generative Adversarial Network for Synthetic Time Series Data Generation in Smart Grids","authors":"Chi Zhang, S. Kuppannagari, R. Kannan, V. Prasanna","doi":"10.1109/SmartGridComm.2018.8587464","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2018.8587464","url":null,"abstract":"The availability of fine grained time series data is a pre-requisite for research in smart-grids. While data for transmission systems is relatively easily obtainable, issues related to data collection, security and privacy hinder the widespread public availability/accessibility of such datasets at the distribution system level. This has prevented the larger research community from effectively applying sophisticated machine learning algorithms to significantly improve the distribution-level accuracy of predictions and increase the efficiency of grid operations. Synthetic dataset generation has proven to be a promising solution for addressing data availability issues in various domains such as computer vision, natural language processing and medicine. However, its exploration in the smart grid context remains unsatisfactory. Previous works have tried to generate synthetic datasets by modeling the underlying system dynamics: an approach which is difficult, time consuming, error prone and often times infeasible in many problems. In this work, we propose a novel data-driven approach to synthetic dataset generation by utilizing deep generative adversarial networks (GAN) to learn the conditional probability distribution of essential features in the real dataset and generate samples based on the learned distribution. To evaluate our synthetically generated dataset, we measure the maximum mean discrepancy (MMD) between real and synthetic datasets as probability distributions, and show that their sampling distance converges. To further validate our synthetic dataset, we perform common smart grid tasks such as k-means clustering and short-term prediction on both datasets. Experimental results show the efficacy of our synthetic dataset approach: the real and synthetic datasets are indistinguishable by solely examining the output of these tasks.","PeriodicalId":213523,"journal":{"name":"2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132180973","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 : 2018-10-01DOI: 10.1109/SmartGridComm.2018.8587463
P. Kong, K. Tseng, Joe-Air Jiang, Chih-Wen Liu
Based on topological and geometrical data of an actual national power grid, this paper first presents the statistical characteristics of a transmission line monitoring wireless sensor network (WSN). We have discovered that the length of a transmission line follows a Weibull distribution. The number of towers in a transmission line has a Negative Binomial distribution, and the distance between two adjacent towers is Gamma distributed. Using this topology information, we further study the robustness of a WSN for transmission line monitoring. Compared to controlling node degree, we have found that robustness can be better achieved by providing multiple node-disjoint paths between a communication node and the control center. We have developed an algorithm to find the minimum communication range in achieving a desired robustness. Through extensive numerical results, we have confirmed that it is possible to guarantee robustness against a single node failure as long as we can provide a communication range of at least 1.09 km.
{"title":"Robust Wireless Sensor Networks for Transmission Line Monitoring in Taiwan","authors":"P. Kong, K. Tseng, Joe-Air Jiang, Chih-Wen Liu","doi":"10.1109/SmartGridComm.2018.8587463","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2018.8587463","url":null,"abstract":"Based on topological and geometrical data of an actual national power grid, this paper first presents the statistical characteristics of a transmission line monitoring wireless sensor network (WSN). We have discovered that the length of a transmission line follows a Weibull distribution. The number of towers in a transmission line has a Negative Binomial distribution, and the distance between two adjacent towers is Gamma distributed. Using this topology information, we further study the robustness of a WSN for transmission line monitoring. Compared to controlling node degree, we have found that robustness can be better achieved by providing multiple node-disjoint paths between a communication node and the control center. We have developed an algorithm to find the minimum communication range in achieving a desired robustness. Through extensive numerical results, we have confirmed that it is possible to guarantee robustness against a single node failure as long as we can provide a communication range of at least 1.09 km.","PeriodicalId":213523,"journal":{"name":"2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"11 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133925070","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 : 2018-10-01DOI: 10.1109/SmartGridComm.2018.8587453
Christophe Patyn, Thijs Peirelinck, Geert Deconinck, A. Nowé
The increasing share of renewable energy sources in the electricity grid results in a higher degree of uncertainty regarding electrical energy production. In response to this, flexibility of the demand has been proposed as part of the solution. An important source of flexibility available at the residential consumer side are thermostatically controlled loads (TCLs). In this paper the activation of this source of flexibility is achieved by applying batch reinforcement learning (BRL) to an electric water heater (EWH) in a Time of Use (ToU) setting. The cost performance of six BRL agents with six different state spaces is compared quantitatively. In every case, the BRL agent can successfully shift energy consumption within 20–25 days. The performance of an agent with access to multiple temperature sensors along the height of the EWH is comparable to the performance of an agent with access to only the highest temperature sensor. This indicates manufacturing costs related to sensors can be reduced while maintaining the same performance. Additionally, results show that the inclusion of a theoretical state of charge value in the state space increases performance by more than 8% compared to the performance of the other BRL agents. It is therefore argued that an estimation of the state of charge should be included in future work as it would increase cost performance.
{"title":"Intelligent Electric Water Heater Control with Varying State Information","authors":"Christophe Patyn, Thijs Peirelinck, Geert Deconinck, A. Nowé","doi":"10.1109/SmartGridComm.2018.8587453","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2018.8587453","url":null,"abstract":"The increasing share of renewable energy sources in the electricity grid results in a higher degree of uncertainty regarding electrical energy production. In response to this, flexibility of the demand has been proposed as part of the solution. An important source of flexibility available at the residential consumer side are thermostatically controlled loads (TCLs). In this paper the activation of this source of flexibility is achieved by applying batch reinforcement learning (BRL) to an electric water heater (EWH) in a Time of Use (ToU) setting. The cost performance of six BRL agents with six different state spaces is compared quantitatively. In every case, the BRL agent can successfully shift energy consumption within 20–25 days. The performance of an agent with access to multiple temperature sensors along the height of the EWH is comparable to the performance of an agent with access to only the highest temperature sensor. This indicates manufacturing costs related to sensors can be reduced while maintaining the same performance. Additionally, results show that the inclusion of a theoretical state of charge value in the state space increases performance by more than 8% compared to the performance of the other BRL agents. It is therefore argued that an estimation of the state of charge should be included in future work as it would increase cost performance.","PeriodicalId":213523,"journal":{"name":"2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133212345","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 : 2018-10-01DOI: 10.1109/SmartGridComm.2018.8587601
T. Tesfay, Mahdi Jamei, A. Scaglione, Mojdeh Khorsand, K. Hedman, R. Bazzi
To address the need for trusted information across bulk power systems, our paper proposes a new type of distributed ledger (or Blockchain), for a shared management of sensitive information in power systems. We call our Blockchain design the Assured Volt Ampere Information Ledger (AVAIL). The AVAIL` abstractions fit data needs of prototypical grid applications in wide area protection and control, energy management systems, and markets. The contribution of this paper is to draw directly from the distinct requirements of these applications and the valid assumptions about the adversaries, to shape the AVAIL abstractions. AVAIL is unique for the following features: 1) Adversarial model: Our design principles consider an adversarial model where attacks affect physical resources; 2) Non-binary validity: in our setting we allow for a spectrum of validity; 3) Validity enforcement: validity in our setting is governed by physical laws.
{"title":"AVAIL: Assured Volt-Ampère Information Ledger","authors":"T. Tesfay, Mahdi Jamei, A. Scaglione, Mojdeh Khorsand, K. Hedman, R. Bazzi","doi":"10.1109/SmartGridComm.2018.8587601","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2018.8587601","url":null,"abstract":"To address the need for trusted information across bulk power systems, our paper proposes a new type of distributed ledger (or Blockchain), for a shared management of sensitive information in power systems. We call our Blockchain design the Assured Volt Ampere Information Ledger (AVAIL). The AVAIL` abstractions fit data needs of prototypical grid applications in wide area protection and control, energy management systems, and markets. The contribution of this paper is to draw directly from the distinct requirements of these applications and the valid assumptions about the adversaries, to shape the AVAIL abstractions. AVAIL is unique for the following features: 1) Adversarial model: Our design principles consider an adversarial model where attacks affect physical resources; 2) Non-binary validity: in our setting we allow for a spectrum of validity; 3) Validity enforcement: validity in our setting is governed by physical laws.","PeriodicalId":213523,"journal":{"name":"2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129339066","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 : 2018-10-01DOI: 10.1109/SmartGridComm.2018.8587527
Xingyu Gao, Kui Wang, Chenye Wu
To tackle the challenges brought by the renewable’s stochastic nature, activities have been picked up: FERC order 755 requires ISOs to introduce mileage payment to frequency regulation providers for more reliable and high quality services. This payment is currently being collected from the ISOs despite the fact that it is not ISOs who cause this extra payment. Therefore, we submit that it is time to reconsider a fair cost allocation. In particular, we study the impact of introducing the corresponding ‘mileage cost’ to the renewables for causing fluctuations in the system. We start by formulating the problem with perfect forecasting for an infinite horizon. Then, we investigate the role of information by restricting our knowledge within a window, i.e., the Model Predictive Control (MPC) approach. We prove that the MPC approach can achieve near optimal performance and further characterize the performance guarantee. Finally, we propose a hierarchical control approach to initiate the discussion on sharing, coordination, and privacy.
{"title":"Who Should Pay for the Mileage Payment?","authors":"Xingyu Gao, Kui Wang, Chenye Wu","doi":"10.1109/SmartGridComm.2018.8587527","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2018.8587527","url":null,"abstract":"To tackle the challenges brought by the renewable’s stochastic nature, activities have been picked up: FERC order 755 requires ISOs to introduce mileage payment to frequency regulation providers for more reliable and high quality services. This payment is currently being collected from the ISOs despite the fact that it is not ISOs who cause this extra payment. Therefore, we submit that it is time to reconsider a fair cost allocation. In particular, we study the impact of introducing the corresponding ‘mileage cost’ to the renewables for causing fluctuations in the system. We start by formulating the problem with perfect forecasting for an infinite horizon. Then, we investigate the role of information by restricting our knowledge within a window, i.e., the Model Predictive Control (MPC) approach. We prove that the MPC approach can achieve near optimal performance and further characterize the performance guarantee. Finally, we propose a hierarchical control approach to initiate the discussion on sharing, coordination, and privacy.","PeriodicalId":213523,"journal":{"name":"2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122101729","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：