Pub Date : 2020-08-01DOI: 10.1109/PMAPS47429.2020.9183702
Dange Huang, B. Bagen
Utilities are facing many challenges in planning and operating the power systems. The use of probabilistic planning approach is a beneficial supplement to the existing system planning and operation process. A series of tools has been developed in Manitoba Hydro to provide inputs to high level decision making process including capital project justification, enhancement of transmission asset management, prioritization of transmission asset investment. The applications of the basic concept that has been used in the risk assessment tools are illustrated through the assessment of a potential investment project involving the replacement of a number of breakers in a practical power system. Particularly the evaluation of the breaker replacement project considers the operational constraints, which is an important aspect that needs to be modelled in practical power system reliability assessment.
{"title":"System Reliability Risk Model and Its Application to Station Breaker Replacement","authors":"Dange Huang, B. Bagen","doi":"10.1109/PMAPS47429.2020.9183702","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183702","url":null,"abstract":"Utilities are facing many challenges in planning and operating the power systems. The use of probabilistic planning approach is a beneficial supplement to the existing system planning and operation process. A series of tools has been developed in Manitoba Hydro to provide inputs to high level decision making process including capital project justification, enhancement of transmission asset management, prioritization of transmission asset investment. The applications of the basic concept that has been used in the risk assessment tools are illustrated through the assessment of a potential investment project involving the replacement of a number of breakers in a practical power system. Particularly the evaluation of the breaker replacement project considers the operational constraints, which is an important aspect that needs to be modelled in practical power system reliability assessment.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131872342","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 : 2020-08-01DOI: 10.1109/PMAPS47429.2020.9183550
S. Hajeforosh, M. Bollen
Load growth and addition of renewable energy generation occur in a way that makes the power grid being operated closer to its physical limits. Increasing the flexibility of the electrical power system is an essential step in ensuring a continued high reliability of the electricity supply. Dynamic line rating (DLR) can be utilized to increase the reliability of the system by ordering curtailment only when needed. In this paper, a probabilistic approach is introduced for the operational overload protection based on the probability distribution of the actual line capacity. That distribution is obtained by considering measurement and prediction errors in weather parameters as well as other uncertainties. The results indicate that probabilistic DLR based protection would allow operational decision-making based on a fair balance between dependability and security. This is not possible using the classical overload protection system.
{"title":"Transmission Line Overloading Analysis Using Probabilistic Dynamic Line Rating","authors":"S. Hajeforosh, M. Bollen","doi":"10.1109/PMAPS47429.2020.9183550","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183550","url":null,"abstract":"Load growth and addition of renewable energy generation occur in a way that makes the power grid being operated closer to its physical limits. Increasing the flexibility of the electrical power system is an essential step in ensuring a continued high reliability of the electricity supply. Dynamic line rating (DLR) can be utilized to increase the reliability of the system by ordering curtailment only when needed. In this paper, a probabilistic approach is introduced for the operational overload protection based on the probability distribution of the actual line capacity. That distribution is obtained by considering measurement and prediction errors in weather parameters as well as other uncertainties. The results indicate that probabilistic DLR based protection would allow operational decision-making based on a fair balance between dependability and security. This is not possible using the classical overload protection system.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122619396","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 : 2020-08-01DOI: 10.1109/PMAPS47429.2020.9183481
Atri Bera, Anurag Chowdhury, J. Mitra, Saleh S Almasabi, M. Benidris
The penetration of renewable energy sources (RES) and energy storage systems (ESS) in the modern-day power grid is increasing at a fast pace. However, reliability assessment of power systems using traditional methods has become a challenging task due to the interdependencies between RES like wind and solar, ESS, and the load. This paper proposes a new method based on artificial neural networks (ANN), a data-driven technique, for reliability assessment of a power system by estimating the parameters of the ANN. The hourly generation data of the distributed and conventional generators are considered to be the features or the input variables. A recurrent neural network based classification algorithm is trained to determine system responses to changes in system conditions. The data required for training and testing the learning algorithm is generated using sequential Monte Carlo simulation. The IEEE Reliability Test System is utilized for testing and validating the proposed approach. The results indicate that the learning algorithm can model the temporal relevance between different system variables for successful reliability assessment of the system.
{"title":"Data-driven Assessment of Power System Reliability in Presence of Renewable Energy","authors":"Atri Bera, Anurag Chowdhury, J. Mitra, Saleh S Almasabi, M. Benidris","doi":"10.1109/PMAPS47429.2020.9183481","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183481","url":null,"abstract":"The penetration of renewable energy sources (RES) and energy storage systems (ESS) in the modern-day power grid is increasing at a fast pace. However, reliability assessment of power systems using traditional methods has become a challenging task due to the interdependencies between RES like wind and solar, ESS, and the load. This paper proposes a new method based on artificial neural networks (ANN), a data-driven technique, for reliability assessment of a power system by estimating the parameters of the ANN. The hourly generation data of the distributed and conventional generators are considered to be the features or the input variables. A recurrent neural network based classification algorithm is trained to determine system responses to changes in system conditions. The data required for training and testing the learning algorithm is generated using sequential Monte Carlo simulation. The IEEE Reliability Test System is utilized for testing and validating the proposed approach. The results indicate that the learning algorithm can model the temporal relevance between different system variables for successful reliability assessment of the system.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128557414","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 : 2020-08-01DOI: 10.1109/PMAPS47429.2020.9183424
S. Ekisheva, M. Papic, M. Lauby, B. David Till
This paper presents a first comprehensive statistical study of transmission inventory and outage data for underground AC circuits on the North American scale. The analysis is based on the data collected and submitted by member utilities to the North American Electric Reliability Corporation’s (NERC’s) Transmission Availability Data System (TADS) during the years 2013 to 2018. The statistical approach considers the random nature of automatic outages and connects the outage frequency on an underground circuit and the outage duration with the circuit inventory attributes both numerical and categorical (voltage class, mileage, number of terminals, terrain etc.)A comparative analysis of reliability statistics for underground and overhead transmission circuits is also presented. It shows that automatic outages on the underground circuits are significantly rarer but much longer than on the overhead circuits. The greater durations result in a higher unavailability of the underground transmission lines compared with the overhead ones—on average, an underground AC circuit is unavailable 29 hours a year due to sustained automatic outages versus 6 hours for an overhead ac circuit.
{"title":"Underground AC Circuits in North America: Inventory Attributes and Sustained Outages","authors":"S. Ekisheva, M. Papic, M. Lauby, B. David Till","doi":"10.1109/PMAPS47429.2020.9183424","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183424","url":null,"abstract":"This paper presents a first comprehensive statistical study of transmission inventory and outage data for underground AC circuits on the North American scale. The analysis is based on the data collected and submitted by member utilities to the North American Electric Reliability Corporation’s (NERC’s) Transmission Availability Data System (TADS) during the years 2013 to 2018. The statistical approach considers the random nature of automatic outages and connects the outage frequency on an underground circuit and the outage duration with the circuit inventory attributes both numerical and categorical (voltage class, mileage, number of terminals, terrain etc.)A comparative analysis of reliability statistics for underground and overhead transmission circuits is also presented. It shows that automatic outages on the underground circuits are significantly rarer but much longer than on the overhead circuits. The greater durations result in a higher unavailability of the underground transmission lines compared with the overhead ones—on average, an underground AC circuit is unavailable 29 hours a year due to sustained automatic outages versus 6 hours for an overhead ac circuit.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125170084","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 : 2020-08-01DOI: 10.1109/PMAPS47429.2020.9183697
Haolin Yang, K. Schell
Electricity price forecasting is critical to numerous tasks in the power system such as strategic bidding, generation scheduling, optimal scheduling of storage reserves and system analysis. Most existing price forecasting models focus on hourly prediction for the day ahead market. This work focuses on the real-time, 5-minute market, with the goal of developing a model able to capture both the long- and short-term temporal distribution of the data. Extending the recent advances in deep learning models of time series forecasting, the proposed model - named HFnet - is a novel multi-branch Gated Recurrent Unit (GRU) architecture for electricity price forecasting. Extensive empirical analyses using real-time data from the New York Independent System Operator (NYISO) illustrate the value of the proposed model when compared to state-of-art prediction models, with an average reduction in error of 10%.
{"title":"HFNet: Forecasting Real-Time Electricity Price via Novel GRU Architectures","authors":"Haolin Yang, K. Schell","doi":"10.1109/PMAPS47429.2020.9183697","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183697","url":null,"abstract":"Electricity price forecasting is critical to numerous tasks in the power system such as strategic bidding, generation scheduling, optimal scheduling of storage reserves and system analysis. Most existing price forecasting models focus on hourly prediction for the day ahead market. This work focuses on the real-time, 5-minute market, with the goal of developing a model able to capture both the long- and short-term temporal distribution of the data. Extending the recent advances in deep learning models of time series forecasting, the proposed model - named HFnet - is a novel multi-branch Gated Recurrent Unit (GRU) architecture for electricity price forecasting. Extensive empirical analyses using real-time data from the New York Independent System Operator (NYISO) illustrate the value of the proposed model when compared to state-of-art prediction models, with an average reduction in error of 10%.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117102451","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 : 2020-08-01DOI: 10.1109/PMAPS47429.2020.9183620
A. Côté, O. Blancke, S. Alarie, Amira Dems, D. Komljenovic, D. Messaoudi
To meet new needs and respond to changes in the energy market, Hydro-Québec TransÉnergie required new predictive modelling methods and systems to support its asset management activities. It created PRIAD, a robust integration and decision support system. One goal of PRIAD is to assess asset behavior for the purposes of simulating system reliability. A method using a black-box optimization technique was developed to calibrate an expert reliability model with historical data analysis. The model's electrical equipment reliability predictions were satisfactory, but further improvements are planned. One benefit of this approach is to allow experts to reassess their maintenance strategies using modelling results.
{"title":"Combining Historical Data and Domain Expert Knowledge Using Optimization to Model Electrical Equipment Reliability","authors":"A. Côté, O. Blancke, S. Alarie, Amira Dems, D. Komljenovic, D. Messaoudi","doi":"10.1109/PMAPS47429.2020.9183620","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183620","url":null,"abstract":"To meet new needs and respond to changes in the energy market, Hydro-Québec TransÉnergie required new predictive modelling methods and systems to support its asset management activities. It created PRIAD, a robust integration and decision support system. One goal of PRIAD is to assess asset behavior for the purposes of simulating system reliability. A method using a black-box optimization technique was developed to calibrate an expert reliability model with historical data analysis. The model's electrical equipment reliability predictions were satisfactory, but further improvements are planned. One benefit of this approach is to allow experts to reassess their maintenance strategies using modelling results.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116734622","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 : 2020-08-01DOI: 10.1109/PMAPS47429.2020.9183468
Gregg A. Spindler, Susan L. Spindler
Probabilistic transmission planning and risk assessment has been used for several decades. One key component is the use of historical outage data to compute failure rates of individual transmission lines which are used in the modeling of a transmission system. The most common approach used to calculate transmission line outage probabilities is the assumption of a Poisson distribution of outages in a unit time period, with an Exponential distribution of time between failures. This paper provides a discussion of some factors which influence reliability. It compares two commonly used statistical failure distributions, the Exponential and Weibull applied to a large sample of outage history from individual transmission circuits of US systems.
{"title":"Use of Transmission Line Outage History for Probabilistic Transmission Risk Assessment","authors":"Gregg A. Spindler, Susan L. Spindler","doi":"10.1109/PMAPS47429.2020.9183468","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183468","url":null,"abstract":"Probabilistic transmission planning and risk assessment has been used for several decades. One key component is the use of historical outage data to compute failure rates of individual transmission lines which are used in the modeling of a transmission system. The most common approach used to calculate transmission line outage probabilities is the assumption of a Poisson distribution of outages in a unit time period, with an Exponential distribution of time between failures. This paper provides a discussion of some factors which influence reliability. It compares two commonly used statistical failure distributions, the Exponential and Weibull applied to a large sample of outage history from individual transmission circuits of US systems.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130794085","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 : 2020-08-01DOI: 10.1109/PMAPS47429.2020.9183634
Sylvie Koziel, P. Hilber, R. Ichise
Power grid operators use data to guide their asset management decisions. However, as the complexity of collected data increases with time and amount of sensors, it becomes more difficult to extract relevant information. Therefore, methods that perform detection tasks need to be developed, especially in distribution systems, which are impacted by distributed generation and smart appliances. Until now, methods employed in local power systems for detection purposes using data with low sampling rate, have not been reviewed. This paper provides a literature review focused on anomaly detection, fault location, and load disaggregation. We analyze the methods in terms of their type, data requirements and ways they are implemented. Many belong to the machine learning field. We find that some methods are typically combined with others and perform specific tasks, while other methods are more ubiquitous and often used alone. Continued research is needed to identify how to guide the choice of methods, and to investigate combinations of methods that have not been studied yet.
{"title":"A review of data-driven and probabilistic algorithms for detection purposes in local power systems","authors":"Sylvie Koziel, P. Hilber, R. Ichise","doi":"10.1109/PMAPS47429.2020.9183634","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183634","url":null,"abstract":"Power grid operators use data to guide their asset management decisions. However, as the complexity of collected data increases with time and amount of sensors, it becomes more difficult to extract relevant information. Therefore, methods that perform detection tasks need to be developed, especially in distribution systems, which are impacted by distributed generation and smart appliances. Until now, methods employed in local power systems for detection purposes using data with low sampling rate, have not been reviewed. This paper provides a literature review focused on anomaly detection, fault location, and load disaggregation. We analyze the methods in terms of their type, data requirements and ways they are implemented. Many belong to the machine learning field. We find that some methods are typically combined with others and perform specific tasks, while other methods are more ubiquitous and often used alone. Continued research is needed to identify how to guide the choice of methods, and to investigate combinations of methods that have not been studied yet.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129899737","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 : 2020-08-01DOI: 10.1109/PMAPS47429.2020.9183679
N. Bhusal, Mukesh Gautam, Michael Abdelmalak, M. Benidris
The frequency of disruptive and newly emerging threats (e.g. man-made attacks—cyber and physical attacks; extreme natural events—hurricanes, earthquakes, and floods) has escalated in the last decade. Impacts of these events are very severe ranging from long power outage duration, major power system equipment (e.g. power generation plants, transmission and distribution lines, and substation) destruction, and complete blackout. Accurate modeling of these events is vital as they serve as mathematical tools for the assessment and evaluation of various operations and planning investment strategies to harden power systems against these events. This paper provides a comprehensive and critical review of current practices in modeling of extreme events, system components, and system response for resilience evaluation and enhancement, which is a important stepping stone toward the development of complete, accurate, and computationally attractive modeling techniques. The paper starts with reviewing existing technologies to model the propagation of extreme events and then discusses the approaches used to model impacts of these events on power system components and system response. This paper also discusses the research gaps and associated challenges, and potential solutions to the limitations of the existing modeling approaches.
{"title":"Modeling of Natural Disasters and Extreme Events for Power System Resilience Enhancement and Evaluation Methods","authors":"N. Bhusal, Mukesh Gautam, Michael Abdelmalak, M. Benidris","doi":"10.1109/PMAPS47429.2020.9183679","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183679","url":null,"abstract":"The frequency of disruptive and newly emerging threats (e.g. man-made attacks—cyber and physical attacks; extreme natural events—hurricanes, earthquakes, and floods) has escalated in the last decade. Impacts of these events are very severe ranging from long power outage duration, major power system equipment (e.g. power generation plants, transmission and distribution lines, and substation) destruction, and complete blackout. Accurate modeling of these events is vital as they serve as mathematical tools for the assessment and evaluation of various operations and planning investment strategies to harden power systems against these events. This paper provides a comprehensive and critical review of current practices in modeling of extreme events, system components, and system response for resilience evaluation and enhancement, which is a important stepping stone toward the development of complete, accurate, and computationally attractive modeling techniques. The paper starts with reviewing existing technologies to model the propagation of extreme events and then discusses the approaches used to model impacts of these events on power system components and system response. This paper also discusses the research gaps and associated challenges, and potential solutions to the limitations of the existing modeling approaches.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115797494","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 : 2020-08-01DOI: 10.1109/PMAPS47429.2020.9183505
G. Bolacell, Mauro Augusto da Rosa
Penetration of intermittent renewable generation, power market effects and dynamic conditions are driving the power system to an operation with higher flexibility. The concept of dynamic line rating is envisioned as a solution to enhance transmission system flexibility. This paper proposes to include the dynamic conductor thermal modeling on the composite power system reliability assessment using a sequential Monte Carlo simulation. An hourly capacity series for each transmission line is generated regarding the type of conductor, voltage level and weather conditions, respecting the maximum conductor temperature. The IEEE-RTS 79 is utilized to illustrate the proposed methodology.
{"title":"Composite Power System Reliability Assessment Considering Transmission Line Flexibility","authors":"G. Bolacell, Mauro Augusto da Rosa","doi":"10.1109/PMAPS47429.2020.9183505","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183505","url":null,"abstract":"Penetration of intermittent renewable generation, power market effects and dynamic conditions are driving the power system to an operation with higher flexibility. The concept of dynamic line rating is envisioned as a solution to enhance transmission system flexibility. This paper proposes to include the dynamic conductor thermal modeling on the composite power system reliability assessment using a sequential Monte Carlo simulation. An hourly capacity series for each transmission line is generated regarding the type of conductor, voltage level and weather conditions, respecting the maximum conductor temperature. The IEEE-RTS 79 is utilized to illustrate the proposed methodology.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129417179","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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