Pub Date : 2021-09-15DOI: 10.1109/ISGTLatinAmerica52371.2021.9543012
C. G. Bianchin, Prscila Faco De Melo, Cretan Pires de Oliveira, R. Schmal, Victor Gati, Z. Nadal
Electric vehicles (EVs) are a solution to combat greenhouse gas emissions (GHG) and help renewable integration. The biggest issue with EV in countries where the infrastructure of EV charges is not yet largely implemented is the called “range anxiety”, but in Brazil, this problem has another factor as the high cost of the EV Charger as the vehicles itself. The high cost of EV in Brazil is a limiting factor to growth the infrastructure. This project tackles this difficulty, developing and implementing the electrification of a highway in southern Brazil, which crosses the State of Paraná from east to west, connecting cities with EV infrastructure. This study analyzed the EVs available in the region and used its effective range to calculate the distance between EV charging stations trying to reduce the so called “range anxiety”. This project offers charging stations with several types of connectors (in order not to restrict EVs models), also, to encourage the use of electric vehicles on this electrified road, there is no fee during the project period (2 years).
{"title":"Electrification on the Brazilian Highway: a case study","authors":"C. G. Bianchin, Prscila Faco De Melo, Cretan Pires de Oliveira, R. Schmal, Victor Gati, Z. Nadal","doi":"10.1109/ISGTLatinAmerica52371.2021.9543012","DOIUrl":"https://doi.org/10.1109/ISGTLatinAmerica52371.2021.9543012","url":null,"abstract":"Electric vehicles (EVs) are a solution to combat greenhouse gas emissions (GHG) and help renewable integration. The biggest issue with EV in countries where the infrastructure of EV charges is not yet largely implemented is the called “range anxiety”, but in Brazil, this problem has another factor as the high cost of the EV Charger as the vehicles itself. The high cost of EV in Brazil is a limiting factor to growth the infrastructure. This project tackles this difficulty, developing and implementing the electrification of a highway in southern Brazil, which crosses the State of Paraná from east to west, connecting cities with EV infrastructure. This study analyzed the EVs available in the region and used its effective range to calculate the distance between EV charging stations trying to reduce the so called “range anxiety”. This project offers charging stations with several types of connectors (in order not to restrict EVs models), also, to encourage the use of electric vehicles on this electrified road, there is no fee during the project period (2 years).","PeriodicalId":120262,"journal":{"name":"2021 IEEE PES Innovative Smart Grid Technologies Conference - Latin America (ISGT Latin America)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129649504","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 : 2021-09-15DOI: 10.1109/ISGTLatinAmerica52371.2021.9543058
J. Olivares-Rojas, E. Reyes-Archundia, J. Gutiérrez-Gnecchi, Ismael Molina-Moreno, Jaime Cerda-Jacobo, Arturo Méndez-Patiño
This work presents a comparative study of various embedded database schemes for applications in smart metering systems to determine what the best way to store is and retrieve information in smart meters. The assessment comparative takes into consideration a literature review and diverse database technologies factors. The results obtained can help database administrators to choose the right database not only in the smart grid domain but also in different Internet of Things database applications.
{"title":"A Comparative Assessment of Embedded Databases for Smart Metering Systems","authors":"J. Olivares-Rojas, E. Reyes-Archundia, J. Gutiérrez-Gnecchi, Ismael Molina-Moreno, Jaime Cerda-Jacobo, Arturo Méndez-Patiño","doi":"10.1109/ISGTLatinAmerica52371.2021.9543058","DOIUrl":"https://doi.org/10.1109/ISGTLatinAmerica52371.2021.9543058","url":null,"abstract":"This work presents a comparative study of various embedded database schemes for applications in smart metering systems to determine what the best way to store is and retrieve information in smart meters. The assessment comparative takes into consideration a literature review and diverse database technologies factors. The results obtained can help database administrators to choose the right database not only in the smart grid domain but also in different Internet of Things database applications.","PeriodicalId":120262,"journal":{"name":"2021 IEEE PES Innovative Smart Grid Technologies Conference - Latin America (ISGT Latin America)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115034456","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 : 2021-09-15DOI: 10.1109/ISGTLatinAmerica52371.2021.9543085
Carolyn Goodman, J. Thornburg, S. Ramaswami, J. Mohammadi
Electrical grids are traditionally operated as multi-entity systems with each entity managing a geographical region. The current movement toward energy democratization and decarbonization is resulting in higher penetration of distributed energy resources (DERs) and intermittent, renewable generation. This process in turn is increasing the number of grid entities (agents). The paradigm shift is also fueled by increased adoption of intelligent sensors collecting data and actuators for advanced processing and computing. Predicting the future load of different consumers has become increasingly important for grids as they must balance intermittent generation to meet instantaneous demand. The main challenges in demand forecasting stem from the heterogeneity of loads and their data. Deep learning provides tools to utilize the collected data for predicting future load profiles and anticipating high-demand scenarios. This article presents a deep learning approach for load forecasting of commercial buildings with multiple refrigeration units. It then presents a case study demonstrating the efficacy of this approach for predicting refrigeration and freezer load in food retail stores.
{"title":"Load Forecasting of Food Retail Buildings with Deep Learning","authors":"Carolyn Goodman, J. Thornburg, S. Ramaswami, J. Mohammadi","doi":"10.1109/ISGTLatinAmerica52371.2021.9543085","DOIUrl":"https://doi.org/10.1109/ISGTLatinAmerica52371.2021.9543085","url":null,"abstract":"Electrical grids are traditionally operated as multi-entity systems with each entity managing a geographical region. The current movement toward energy democratization and decarbonization is resulting in higher penetration of distributed energy resources (DERs) and intermittent, renewable generation. This process in turn is increasing the number of grid entities (agents). The paradigm shift is also fueled by increased adoption of intelligent sensors collecting data and actuators for advanced processing and computing. Predicting the future load of different consumers has become increasingly important for grids as they must balance intermittent generation to meet instantaneous demand. The main challenges in demand forecasting stem from the heterogeneity of loads and their data. Deep learning provides tools to utilize the collected data for predicting future load profiles and anticipating high-demand scenarios. This article presents a deep learning approach for load forecasting of commercial buildings with multiple refrigeration units. It then presents a case study demonstrating the efficacy of this approach for predicting refrigeration and freezer load in food retail stores.","PeriodicalId":120262,"journal":{"name":"2021 IEEE PES Innovative Smart Grid Technologies Conference - Latin America (ISGT Latin America)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132998804","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 : 2021-09-15DOI: 10.1109/ISGTLatinAmerica52371.2021.9543056
R. I. da Silva, H. Tatizawa
By 2029, more than 21 GW of wind power capacity will be added in Brazil. The intermittency of this source and the capacity factor of wind farms are not being considered in the specification and design of the generator step-up (GSU) transformers collector. The use of high temperature insulation materials, mainly natural ester insulating liquid, allied with the consideration of a realistic loading profile, can help the optimization of the transformer size, allowing savings in the total losses in annual operating cycles, as well as savings in the initial transformer investment costs. The paper will present a proposal of new characteristics for the specification of GSU transformers for wind farms application.
{"title":"A Proposal of Natural Ester Immersed GSU Transformers for Better Efficiency of Wind Farms and Its Intermittences","authors":"R. I. da Silva, H. Tatizawa","doi":"10.1109/ISGTLatinAmerica52371.2021.9543056","DOIUrl":"https://doi.org/10.1109/ISGTLatinAmerica52371.2021.9543056","url":null,"abstract":"By 2029, more than 21 GW of wind power capacity will be added in Brazil. The intermittency of this source and the capacity factor of wind farms are not being considered in the specification and design of the generator step-up (GSU) transformers collector. The use of high temperature insulation materials, mainly natural ester insulating liquid, allied with the consideration of a realistic loading profile, can help the optimization of the transformer size, allowing savings in the total losses in annual operating cycles, as well as savings in the initial transformer investment costs. The paper will present a proposal of new characteristics for the specification of GSU transformers for wind farms application.","PeriodicalId":120262,"journal":{"name":"2021 IEEE PES Innovative Smart Grid Technologies Conference - Latin America (ISGT Latin America)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133984432","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 : 2021-09-15DOI: 10.1109/ISGTLatinAmerica52371.2021.9542998
Satyaki Banik, Md. Sadman Sakib, S. Chowdhury, Nahid-Al-Masood
The increasing incorporation of renewable energy into the electricity grid is making the grid vulnerable to any contingency as system inertia is decreasing gradually with the growing non-synchronous penetration. This growing proliferation of renewable energy challenges the secure operation of power system. This paper proposes an inertia constraint economic dispatch method which ensures the adequacy of minimum inertia at all dispatch periods. The proposed method protects the system during any contingency in terms of frequency stability. A significant improvement in system frequency nadir and RoCoF is shown, yet keeping enough headroom to provide primary frequency response to the system.
{"title":"Inertia Constrained Economic Dispatch in a Renewable Dominated Power System","authors":"Satyaki Banik, Md. Sadman Sakib, S. Chowdhury, Nahid-Al-Masood","doi":"10.1109/ISGTLatinAmerica52371.2021.9542998","DOIUrl":"https://doi.org/10.1109/ISGTLatinAmerica52371.2021.9542998","url":null,"abstract":"The increasing incorporation of renewable energy into the electricity grid is making the grid vulnerable to any contingency as system inertia is decreasing gradually with the growing non-synchronous penetration. This growing proliferation of renewable energy challenges the secure operation of power system. This paper proposes an inertia constraint economic dispatch method which ensures the adequacy of minimum inertia at all dispatch periods. The proposed method protects the system during any contingency in terms of frequency stability. A significant improvement in system frequency nadir and RoCoF is shown, yet keeping enough headroom to provide primary frequency response to the system.","PeriodicalId":120262,"journal":{"name":"2021 IEEE PES Innovative Smart Grid Technologies Conference - Latin America (ISGT Latin America)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128298032","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 : 2021-05-14DOI: 10.1109/ISGTLatinAmerica52371.2021.9543064
J. C. Saire, Joseph Roque, Franco Canziani
In this paper, a smart microgrid implemented in Paracas, Ica, Peru, composed of 6 kWp PV + 6 kW Wind and that provides electricity to a rural community of 40 families, was studied using a data science approach. Real data of solar irradiance, wind speed, energy demand, and voltage of the battery bank from 2 periods of operation were studied to find patterns, seasonality, and existing correlations between the analyzed data. Among the main results are the periodicity of renewable resources and demand, the weekly behavior of electricity demand and how it has progressively increased from an average of 0.7 kW in 2019 to 1.2 kW in 2021, and how power outages are repeated at certain hours in the morning when resources are low or there is a failure in the battery bank. These analyzed data will be used to improve sizing techniques and provide recommendations for energy management to optimize the performance of smart microgrids.
{"title":"Study Of A Hybrid Photovoltaic-Wind Smart Microgrid Using Data Science Approach","authors":"J. C. Saire, Joseph Roque, Franco Canziani","doi":"10.1109/ISGTLatinAmerica52371.2021.9543064","DOIUrl":"https://doi.org/10.1109/ISGTLatinAmerica52371.2021.9543064","url":null,"abstract":"In this paper, a smart microgrid implemented in Paracas, Ica, Peru, composed of 6 kWp PV + 6 kW Wind and that provides electricity to a rural community of 40 families, was studied using a data science approach. Real data of solar irradiance, wind speed, energy demand, and voltage of the battery bank from 2 periods of operation were studied to find patterns, seasonality, and existing correlations between the analyzed data. Among the main results are the periodicity of renewable resources and demand, the weekly behavior of electricity demand and how it has progressively increased from an average of 0.7 kW in 2019 to 1.2 kW in 2021, and how power outages are repeated at certain hours in the morning when resources are low or there is a failure in the battery bank. These analyzed data will be used to improve sizing techniques and provide recommendations for energy management to optimize the performance of smart microgrids.","PeriodicalId":120262,"journal":{"name":"2021 IEEE PES Innovative Smart Grid Technologies Conference - Latin America (ISGT Latin America)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133345641","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 : 2021-04-15DOI: 10.1109/ISGTLatinAmerica52371.2021.9543031
M. Basnet, Subash Poudyal, M. Ali, D. Dasgupta
The Supervisory control and data acquisition (SCADA) systems have been continuously leveraging the evolution of network architecture, communication protocols, next-generation communication techniques (5G, 6G, Wi-Fi 6), and the internet of things (IoT). However, SCADA system has become the most profitable and alluring target for ransomware attackers. This paper proposes the deep learning-based novel ransomware detection framework in the SCADA controlled electric vehicle charging station (EVCS) with the performance analysis of three deep learning algorithms, namely deep neural network (DNN), 1D convolution neural network (CNN), and long short-term memory (LSTM) recurrent neural network. All three-deep learning-based simulated frameworks achieve around 97% average accuracy (ACC), more than 98% of the average area under the curve (AUC) and an average F1-score under 10-fold stratified cross-validation with an average false alarm rate (FAR) less than 1.88%. Ransomware driven distributed denial of service (DDoS) attack tends to shift the state of charge (SOC) profile by exceeding the SOC control thresholds. Also, ransomware driven false data injection (FDI) attack has the potential to damage the entire BES or physical system by manipulating the SOC control thresholds. It's a design choice and optimization issue that a deep learning algorithm can deploy based on the tradeoffs between performance metrics.
{"title":"Ransomware Detection Using Deep Learning in the SCADA System of Electric Vehicle Charging Station","authors":"M. Basnet, Subash Poudyal, M. Ali, D. Dasgupta","doi":"10.1109/ISGTLatinAmerica52371.2021.9543031","DOIUrl":"https://doi.org/10.1109/ISGTLatinAmerica52371.2021.9543031","url":null,"abstract":"The Supervisory control and data acquisition (SCADA) systems have been continuously leveraging the evolution of network architecture, communication protocols, next-generation communication techniques (5G, 6G, Wi-Fi 6), and the internet of things (IoT). However, SCADA system has become the most profitable and alluring target for ransomware attackers. This paper proposes the deep learning-based novel ransomware detection framework in the SCADA controlled electric vehicle charging station (EVCS) with the performance analysis of three deep learning algorithms, namely deep neural network (DNN), 1D convolution neural network (CNN), and long short-term memory (LSTM) recurrent neural network. All three-deep learning-based simulated frameworks achieve around 97% average accuracy (ACC), more than 98% of the average area under the curve (AUC) and an average F1-score under 10-fold stratified cross-validation with an average false alarm rate (FAR) less than 1.88%. Ransomware driven distributed denial of service (DDoS) attack tends to shift the state of charge (SOC) profile by exceeding the SOC control thresholds. Also, ransomware driven false data injection (FDI) attack has the potential to damage the entire BES or physical system by manipulating the SOC control thresholds. It's a design choice and optimization issue that a deep learning algorithm can deploy based on the tradeoffs between performance metrics.","PeriodicalId":120262,"journal":{"name":"2021 IEEE PES Innovative Smart Grid Technologies Conference - Latin America (ISGT Latin America)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127164376","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 : 2021-04-13DOI: 10.1109/ISGTLatinAmerica52371.2021.9543006
Shweta Dahale, B. Natarajan
Limited measurement availability at the distribution grid presents challenges for state estimation and situational awareness. This paper combines the advantages of two sparsity-based state estimation approaches (matrix completion and compressive sensing) that have been proposed recently to address the challenge of unobservability. The proposed approach exploits both the low rank structure and a suitable transform domain representation to leverage the correlation structure of the spatio-temporal data matrix while incorporating the powerflow constraints of the distribution grid. Simulations are carried out on three phase unbalanced IEEE 37 test system to verify the effectiveness of the proposed approach. The performance results reveal - (1) the superiority over traditional matrix completion and (2) very low state estimation errors for high compression ratios representing very low observability.
{"title":"Joint Matrix Completion and Compressed Sensing for State Estimation in Low-observable Distribution System","authors":"Shweta Dahale, B. Natarajan","doi":"10.1109/ISGTLatinAmerica52371.2021.9543006","DOIUrl":"https://doi.org/10.1109/ISGTLatinAmerica52371.2021.9543006","url":null,"abstract":"Limited measurement availability at the distribution grid presents challenges for state estimation and situational awareness. This paper combines the advantages of two sparsity-based state estimation approaches (matrix completion and compressive sensing) that have been proposed recently to address the challenge of unobservability. The proposed approach exploits both the low rank structure and a suitable transform domain representation to leverage the correlation structure of the spatio-temporal data matrix while incorporating the powerflow constraints of the distribution grid. Simulations are carried out on three phase unbalanced IEEE 37 test system to verify the effectiveness of the proposed approach. The performance results reveal - (1) the superiority over traditional matrix completion and (2) very low state estimation errors for high compression ratios representing very low observability.","PeriodicalId":120262,"journal":{"name":"2021 IEEE PES Innovative Smart Grid Technologies Conference - Latin America (ISGT Latin America)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123668179","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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