Pub Date : 2020-08-01DOI: 10.1109/PMAPS47429.2020.9183575
Arne Groß, Antonia Lenders, T. Zech, C. Wittwer, M. Diehl
In the coming years, the energy system will be transformed from central carbon-based power plants to decentralized renewable generation. Due to the dependency of these systems on external influences such as the weather, forecast uncertainties pose a problem. In this paper, we will compare different methods that mitigate the impact of these forecast uncertainties. Our results suggest that estimating these uncertainties and modeling them for optimization can increase the benefit for the individual system.
{"title":"Using Probabilistic Forecasts in Stochastic Optimization","authors":"Arne Groß, Antonia Lenders, T. Zech, C. Wittwer, M. Diehl","doi":"10.1109/PMAPS47429.2020.9183575","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183575","url":null,"abstract":"In the coming years, the energy system will be transformed from central carbon-based power plants to decentralized renewable generation. Due to the dependency of these systems on external influences such as the weather, forecast uncertainties pose a problem. In this paper, we will compare different methods that mitigate the impact of these forecast uncertainties. Our results suggest that estimating these uncertainties and modeling them for optimization can increase the benefit for the individual 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":"121032944","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/PMAPS47429.2020.9183663
M. J. Chihota, Lolo Buys, C. T. Gaunt, B. Bekker
Power system variables such as load demand, power generation from variable renewable energy plants and the placement of future distributed energy resources are characterized by uncertainty. Probabilistic network design based on probabilistic load flow techniques (PLF) is increasingly useful. However, most reported work on this topic focuses on developing faster and more robust statistical formulations of PLF algorithms. Such a unilateral approach diminishes the importance of understanding how the commonly assumed probabilistic load models are derived, and how the PLF analysis leads to planning decisions. This paper details a holistic approach to probabilistic network design for medium voltage (MV) distribution systems based on developing experience in South Africa. The framework includes the derivation of load models from measurements, their application in network analysis, and the interpretation of statistical outputs for planning decisions. The findings have implications for data preparation and the application of the results in MV network planning.
{"title":"Practical Probabilistic Design Procedures for Medium Voltage Distribution Systems","authors":"M. J. Chihota, Lolo Buys, C. T. Gaunt, B. Bekker","doi":"10.1109/PMAPS47429.2020.9183663","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183663","url":null,"abstract":"Power system variables such as load demand, power generation from variable renewable energy plants and the placement of future distributed energy resources are characterized by uncertainty. Probabilistic network design based on probabilistic load flow techniques (PLF) is increasingly useful. However, most reported work on this topic focuses on developing faster and more robust statistical formulations of PLF algorithms. Such a unilateral approach diminishes the importance of understanding how the commonly assumed probabilistic load models are derived, and how the PLF analysis leads to planning decisions. This paper details a holistic approach to probabilistic network design for medium voltage (MV) distribution systems based on developing experience in South Africa. The framework includes the derivation of load models from measurements, their application in network analysis, and the interpretation of statistical outputs for planning decisions. The findings have implications for data preparation and the application of the results in MV network planning.","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":"115217213","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/PMAPS47429.2020.9183603
Katrine Bruvik, Lars Martin Hytten
This paper gives a detailed account of current practices within probabilistic long-term reliability assessment at the Norwegian Transmission System Operator (TSO), Statnett. The core of recent advancements, a comprehensive self-developed Monte Carlo based simulation tool for component failures, is briefly presented along with some comments on the development process. Practical examples included in the paper demonstrates how the simulation tool aid power system analysts toward a deeper, more fundamental insight to power system reliability previously unavailable. In addition, a method to quantify the value of reliability through the probabilistic reliability index Cost of Energy Not Served (CENS) is presented, providing a valuable contribution to improved socio-economic cost benefit analysis in power system planning. The big potential of probabilistic reliability assessment within many TSO related tasks motivates the continued development of the simulation tool and associated methods for reliability analysis. Selected plans at Statnett are therefore also included in this paper.
{"title":"Probabilistic Reliability Analysis in the Norwegian Transmission System","authors":"Katrine Bruvik, Lars Martin Hytten","doi":"10.1109/PMAPS47429.2020.9183603","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183603","url":null,"abstract":"This paper gives a detailed account of current practices within probabilistic long-term reliability assessment at the Norwegian Transmission System Operator (TSO), Statnett. The core of recent advancements, a comprehensive self-developed Monte Carlo based simulation tool for component failures, is briefly presented along with some comments on the development process. Practical examples included in the paper demonstrates how the simulation tool aid power system analysts toward a deeper, more fundamental insight to power system reliability previously unavailable. In addition, a method to quantify the value of reliability through the probabilistic reliability index Cost of Energy Not Served (CENS) is presented, providing a valuable contribution to improved socio-economic cost benefit analysis in power system planning. The big potential of probabilistic reliability assessment within many TSO related tasks motivates the continued development of the simulation tool and associated methods for reliability analysis. Selected plans at Statnett are therefore also included in this paper.","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":"128703332","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/PMAPS47429.2020.9183520
Selmane Dakir, Sélim El Mekki, B. Cornélusse
The sizing process of microgrids requires to run multiple simulations that can be computationally intensive depending on the desired accuracy. An effective way to reduce the simulation time is to compress the available data by selecting representative days from the list of days to be evaluated, such as the 365 days of a year, and assigning them a weight. The aim of this paper is to determine a recommended number of representative days for the sizing of microgrids with an industrial load profile. To this end, real load profiles were collected and analyzed from 22 companies. A sensitivity analysis on the optimal sizing identified according to the number of representative days is carried out for two representative days selection methods. A reliability indicator is proposed and allows to show that, with an optimization-based selection method, 10 representative days are enough on average to characterize the system.
{"title":"On the number of representative days for sizing microgrids with an industrial load profile","authors":"Selmane Dakir, Sélim El Mekki, B. Cornélusse","doi":"10.1109/PMAPS47429.2020.9183520","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183520","url":null,"abstract":"The sizing process of microgrids requires to run multiple simulations that can be computationally intensive depending on the desired accuracy. An effective way to reduce the simulation time is to compress the available data by selecting representative days from the list of days to be evaluated, such as the 365 days of a year, and assigning them a weight. The aim of this paper is to determine a recommended number of representative days for the sizing of microgrids with an industrial load profile. To this end, real load profiles were collected and analyzed from 22 companies. A sensitivity analysis on the optimal sizing identified according to the number of representative days is carried out for two representative days selection methods. A reliability indicator is proposed and allows to show that, with an optimization-based selection method, 10 representative days are enough on average to characterize 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":"120893330","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/PMAPS47429.2020.9183488
J. Pessanha, A. Melo, R. Caldas, D. Falcão
A good solar power photovoltaic generation forecast depends on good quality time series data from measurements of global horizontal irradiation and solar generation. However, measurement system failures and errors in data handling can corrupt data records with gaps and outliers that undermine forecasting accuracy. Therefore, it is important that the fitting of solar energy prediction models must be preceded by a data analysis in order to detect and correct measurement errors. This paper presents the main features of an approach for the joint data cleaning of solar photovoltaic generation and solar irradiation. The methodology comprises 5 chained steps and consists in the combined treatment of global horizontal irradiation and solar photovoltaic generation data using statistical techniques, data mining algorithms and reanalysis data with the purpose of correcting outliers, replacing incorrect values and filling data gaps. The application of the proposed approach is illustrated with a real system presenting good performance.
{"title":"A Methodology for Joint Data Cleaning of Solar Photovoltaic Generation and Solar Irradiation","authors":"J. Pessanha, A. Melo, R. Caldas, D. Falcão","doi":"10.1109/PMAPS47429.2020.9183488","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183488","url":null,"abstract":"A good solar power photovoltaic generation forecast depends on good quality time series data from measurements of global horizontal irradiation and solar generation. However, measurement system failures and errors in data handling can corrupt data records with gaps and outliers that undermine forecasting accuracy. Therefore, it is important that the fitting of solar energy prediction models must be preceded by a data analysis in order to detect and correct measurement errors. This paper presents the main features of an approach for the joint data cleaning of solar photovoltaic generation and solar irradiation. The methodology comprises 5 chained steps and consists in the combined treatment of global horizontal irradiation and solar photovoltaic generation data using statistical techniques, data mining algorithms and reanalysis data with the purpose of correcting outliers, replacing incorrect values and filling data gaps. The application of the proposed approach is illustrated with a real system presenting good performance.","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":"116276577","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/PMAPS47429.2020.9183388
Keyi Wang, K. Kopsidas
Ageing of power network assets and increasing demand challenge the utilities to economically and securely utilise the assets. The challenge of effective utilisation of assets becomes worse under unfavourable thermal conditions, which is the case of underground power cables crossing a road. There are occasions that higher conductor temperatures (10-20°C) can be developed, with a negative effect on cable’s life and failure risks when no remedial actions are taken. One common practice is cable de-rating, however, sacrificing cable’s power capacity. At present, the negative impact of the unfavourable conditions and of de-rating strategies has not been addressed holistically. Hence, the combining effect on failure risks and adequacy is not effectively optimised. To address this gap and quantify the impact of hot spots on networks with various cable designs, this work integrates the thermal modelling of cable’s hot spots and their associated risk of failure into a unified network reliability analysis framework. The methodology is demonstrated in a modified IEEE 14-bus network, to allow the inclusion of cable hot spots. It is found that the system performance with no remedial actions on hot spots is significantly weakened. An optimal de-rating hot spot strategy by 44% to 70% is more effective instead of complete negation (i.e. 100% de-rating factor). This proposed factor considers the holistic network-asset aspects of system adequacy, ageing and failures.
{"title":"Modelling Network Reliability Considering Underground Cable Hot Spot Failures","authors":"Keyi Wang, K. Kopsidas","doi":"10.1109/PMAPS47429.2020.9183388","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183388","url":null,"abstract":"Ageing of power network assets and increasing demand challenge the utilities to economically and securely utilise the assets. The challenge of effective utilisation of assets becomes worse under unfavourable thermal conditions, which is the case of underground power cables crossing a road. There are occasions that higher conductor temperatures (10-20°C) can be developed, with a negative effect on cable’s life and failure risks when no remedial actions are taken. One common practice is cable de-rating, however, sacrificing cable’s power capacity. At present, the negative impact of the unfavourable conditions and of de-rating strategies has not been addressed holistically. Hence, the combining effect on failure risks and adequacy is not effectively optimised. To address this gap and quantify the impact of hot spots on networks with various cable designs, this work integrates the thermal modelling of cable’s hot spots and their associated risk of failure into a unified network reliability analysis framework. The methodology is demonstrated in a modified IEEE 14-bus network, to allow the inclusion of cable hot spots. It is found that the system performance with no remedial actions on hot spots is significantly weakened. An optimal de-rating hot spot strategy by 44% to 70% is more effective instead of complete negation (i.e. 100% de-rating factor). This proposed factor considers the holistic network-asset aspects of system adequacy, ageing and failures.","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":"121520568","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/PMAPS47429.2020.9183593
Giulia De Zotti, Hanne Binder, A. Hansen, H. Madsen, R. Relan
In power systems, electrical consumers can become a significant source of flexibility, by adjusting their consumption according to grid’s needs while respecting their operational constraints. Consumers’ flexibility potential can be exploited through the submission of dynamic electricity prices. Such prices are able to describe the variable condition of the power system and are broadcast to the consumers in order to obtain a certain change in consumption. The formulation of effective dynamic prices requires the development of proper models that describe the price responsiveness of electrical consumers. In this paper, we propose a nonlinear prediction model for the dynamic electricity prices in demand response (DR) programs. Specifically, the nonlinear auto-regressive with exogenous input (NARX) model structure is used to learn from available data to predict appropriate electricity price signals. For the validation of the model (in an aggregate manner) in predicting consumers’ price-response, the data from 10 Danish households is utilised, which has provided by the Danish Transmission Service Operator (TSO) Energinet.
{"title":"Data-driven Nonlinear Prediction Model for Price Signals in Demand Response Programs","authors":"Giulia De Zotti, Hanne Binder, A. Hansen, H. Madsen, R. Relan","doi":"10.1109/PMAPS47429.2020.9183593","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183593","url":null,"abstract":"In power systems, electrical consumers can become a significant source of flexibility, by adjusting their consumption according to grid’s needs while respecting their operational constraints. Consumers’ flexibility potential can be exploited through the submission of dynamic electricity prices. Such prices are able to describe the variable condition of the power system and are broadcast to the consumers in order to obtain a certain change in consumption. The formulation of effective dynamic prices requires the development of proper models that describe the price responsiveness of electrical consumers. In this paper, we propose a nonlinear prediction model for the dynamic electricity prices in demand response (DR) programs. Specifically, the nonlinear auto-regressive with exogenous input (NARX) model structure is used to learn from available data to predict appropriate electricity price signals. For the validation of the model (in an aggregate manner) in predicting consumers’ price-response, the data from 10 Danish households is utilised, which has provided by the Danish Transmission Service Operator (TSO) Energinet.","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":"115937448","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/PMAPS47429.2020.9183703
Marcos Netto, V. Krishnan, L. Mili, Pranav Sharma, V. Ajjarapu
We articulate the reason why dynamic state estimation is needed to push the boundaries of the modal analysis of electric power grids in real-time operation. Then, we demonstrate how to unravel linear and nonlinear modes by using the extended dynamic mode decomposition along with estimates of the synchronous generators’ rotor angles and rotor speed deviations from nominal speed. The estimated modes are associated with electromechanical oscillations that take place continuously in electric power grids because of imbalances between power generation and demand. The numerical simulations are performed on a synthetic, albeit realistic, 2,000-bus network that was designed to resemble the electric power grid of Texas.
{"title":"Real-Time Modal Analysis of Electric Power Grids– The Need for Dynamic State Estimation","authors":"Marcos Netto, V. Krishnan, L. Mili, Pranav Sharma, V. Ajjarapu","doi":"10.1109/PMAPS47429.2020.9183703","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183703","url":null,"abstract":"We articulate the reason why dynamic state estimation is needed to push the boundaries of the modal analysis of electric power grids in real-time operation. Then, we demonstrate how to unravel linear and nonlinear modes by using the extended dynamic mode decomposition along with estimates of the synchronous generators’ rotor angles and rotor speed deviations from nominal speed. The estimated modes are associated with electromechanical oscillations that take place continuously in electric power grids because of imbalances between power generation and demand. The numerical simulations are performed on a synthetic, albeit realistic, 2,000-bus network that was designed to resemble the electric power grid of Texas.","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":"133948978","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/PMAPS47429.2020.9183582
G. Bolacell, D. Calado, L. Venturini, D. Issicaba, M. D. da Rosa
Performance and economic aspects must be considered in order to expand the distribution system network adequately. Ideally, the assessment of the continuity of supply, voltage signal quality and loadability should be carried out altogether aiming to support decisions over different planning alternatives. This paper proposes to map the network performance in terms of the availability, voltage signals, technical losses and operational limits for a planning horizon. Economic costs to build seven planning alternatives are also estimated including costs for maintenance, deployment and energy not supplied. The proposed approach combines a three-phase power flow tool and a short-circuit algorithm within a sequential Monte Carlo simulation approach to evaluate performance indices. Results show that a trade-off among criteria such as continuity and power quality is required on choosing the most suitable alternative. The study is applied to the UFSC 16 Node Test Feeder.
{"title":"Distribution System Planning Considering Power Quality, Loadability and Economic Aspects","authors":"G. Bolacell, D. Calado, L. Venturini, D. Issicaba, M. D. da Rosa","doi":"10.1109/PMAPS47429.2020.9183582","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183582","url":null,"abstract":"Performance and economic aspects must be considered in order to expand the distribution system network adequately. Ideally, the assessment of the continuity of supply, voltage signal quality and loadability should be carried out altogether aiming to support decisions over different planning alternatives. This paper proposes to map the network performance in terms of the availability, voltage signals, technical losses and operational limits for a planning horizon. Economic costs to build seven planning alternatives are also estimated including costs for maintenance, deployment and energy not supplied. The proposed approach combines a three-phase power flow tool and a short-circuit algorithm within a sequential Monte Carlo simulation approach to evaluate performance indices. Results show that a trade-off among criteria such as continuity and power quality is required on choosing the most suitable alternative. The study is applied to the UFSC 16 Node Test Feeder.","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":"114718228","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/PMAPS47429.2020.9183485
E. Moreira, E. Chagas, A. Rodrigues, M. da Guia da Silva
The problem of power flow in islanded operation of microgrids is more complex than in interconnected mode due to the absence of a slack bus. In addition, the power flow must consider uncertainties in a probabilistic framework. Probabilistic power flow has a high computational cost due to the need to assess a large number of states and the use of iterative methods for nonlinear systems. Consequently, it is interesting to develop linear power flow algorithms for islanded microgrids. The main aim of this paper is to propose a linear power flow algorithm based on the Gauss Zbus method for islanded microgrids. This algorithm was used to evaluate voltages and angular frequency in a probabilistic power flow method based on Monte Carlo Simulation. The test results with the proposed method demonstrated that it achieves high accuracy and significant computational cost savings (about of 99%) over the Newton-Raphson Method.
{"title":"Linear Probabilistic Power Flow for Islanded Microgrids","authors":"E. Moreira, E. Chagas, A. Rodrigues, M. da Guia da Silva","doi":"10.1109/PMAPS47429.2020.9183485","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183485","url":null,"abstract":"The problem of power flow in islanded operation of microgrids is more complex than in interconnected mode due to the absence of a slack bus. In addition, the power flow must consider uncertainties in a probabilistic framework. Probabilistic power flow has a high computational cost due to the need to assess a large number of states and the use of iterative methods for nonlinear systems. Consequently, it is interesting to develop linear power flow algorithms for islanded microgrids. The main aim of this paper is to propose a linear power flow algorithm based on the Gauss Zbus method for islanded microgrids. This algorithm was used to evaluate voltages and angular frequency in a probabilistic power flow method based on Monte Carlo Simulation. The test results with the proposed method demonstrated that it achieves high accuracy and significant computational cost savings (about of 99%) over the Newton-Raphson Method.","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":"117274724","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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