Pub Date : 2016-10-01DOI: 10.1109/PMAPS.2016.7764081
Jingrui Xie, Tao Hong
Model selection is an important step for both point and probabilistic load forecasting. In the point load forecasting literature and practices, point error measures, such as mean absolute percentage error (MAPE), are often used for model selection. On the other hand, many probabilistic load forecasting methodologies rely on the model selection mechanism developed for point load forecasting. In other words, the models for probabilistic load forecasting are selected to minimize point error measures rather than probabilistic ones, such as quantile score. Intuitively, selecting models for probabilistic forecasting based on a point error measure is less computationally intensive and less accurate than its counterpart. The practical question is whether we can gain significant accuracy by taking the more computationally intensive route. This paper presents a comparative study on model selection for probabilistic load forecasting, using point and probabilistic error measures respectively. The data for the case study is from the load forecasting track of the Global Energy Forecasting Competition 2014. We find that the two model selection mechanisms indeed return different underlying models. While on average, the models from quantile score based model selection method can lead to more accurate probabilistic forecasts, the improvement over the MAPE based model selection method is marginal.
{"title":"Comparing two model selection frameworks for probabilistic load forecasting","authors":"Jingrui Xie, Tao Hong","doi":"10.1109/PMAPS.2016.7764081","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764081","url":null,"abstract":"Model selection is an important step for both point and probabilistic load forecasting. In the point load forecasting literature and practices, point error measures, such as mean absolute percentage error (MAPE), are often used for model selection. On the other hand, many probabilistic load forecasting methodologies rely on the model selection mechanism developed for point load forecasting. In other words, the models for probabilistic load forecasting are selected to minimize point error measures rather than probabilistic ones, such as quantile score. Intuitively, selecting models for probabilistic forecasting based on a point error measure is less computationally intensive and less accurate than its counterpart. The practical question is whether we can gain significant accuracy by taking the more computationally intensive route. This paper presents a comparative study on model selection for probabilistic load forecasting, using point and probabilistic error measures respectively. The data for the case study is from the load forecasting track of the Global Energy Forecasting Competition 2014. We find that the two model selection mechanisms indeed return different underlying models. While on average, the models from quantile score based model selection method can lead to more accurate probabilistic forecasts, the improvement over the MAPE based model selection method is marginal.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115756996","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 : 2016-10-01DOI: 10.1109/PMAPS.2016.7764159
Markus Loschenbrand, M. Korpås
This paper introduces an agent based model for Frequency Activated Reserve Markets. Generation Units (GenUns) bid both prices and quantity in interconnected and dynamically congested Market Areas in order to reach their optimal production point. The units are limited by their spare capacity after their actions on the spot market. Generation Companies (GenCos) manage the strategy portfolios of their subordinate agents with the goal of coordinating the bidding behavior and subsequently increasing profits. A case study of Monte Carlo simulated units will show the dominance of Marginal Cost bidding over different periods and pricing modes (System Price and Pay-as-Bid) as well as the quality of the chosen modeling approach.
{"title":"An agent based model of a frequency activated electricity reserve market","authors":"Markus Loschenbrand, M. Korpås","doi":"10.1109/PMAPS.2016.7764159","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764159","url":null,"abstract":"This paper introduces an agent based model for Frequency Activated Reserve Markets. Generation Units (GenUns) bid both prices and quantity in interconnected and dynamically congested Market Areas in order to reach their optimal production point. The units are limited by their spare capacity after their actions on the spot market. Generation Companies (GenCos) manage the strategy portfolios of their subordinate agents with the goal of coordinating the bidding behavior and subsequently increasing profits. A case study of Monte Carlo simulated units will show the dominance of Marginal Cost bidding over different periods and pricing modes (System Price and Pay-as-Bid) as well as the quality of the chosen modeling approach.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123183901","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 : 2016-10-01DOI: 10.1109/PMAPS.2016.7764106
Liting Tian, Jianbo Guo, Lin Cheng
Energy storage is used to balance the variant power for the stability of the grid. It is significant to understand the fluctuation characteristic of renewable energy (RE) generation and the requirements of energy storage when large-scale RE is integrated in the grid. In the paper, a novel method based on time and frequency domain analysis is proposed for energy storage system (ESS) sizing, including both power sizing and energy sizing. According to the relationship between charge/discharge power and stored energy, the sizing model is established based on autocorrelation function and power spectral density (PSD) of the stochastic cycling process. The time and spectral characteristic of RE generation is analyzed based on the historical generation data of a wind farm and a PV station in the Northwest region of China. The size of energy storage is determined by the time and frequency domain method respectively. Comparing with the time domain method, it is showed that the frequency domain method is sufficient for energy storage sizing with enough accuracy and a much easier calculation process at the same time.
{"title":"A novel method for energy storage sizing based on time and frequency domain analysis","authors":"Liting Tian, Jianbo Guo, Lin Cheng","doi":"10.1109/PMAPS.2016.7764106","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764106","url":null,"abstract":"Energy storage is used to balance the variant power for the stability of the grid. It is significant to understand the fluctuation characteristic of renewable energy (RE) generation and the requirements of energy storage when large-scale RE is integrated in the grid. In the paper, a novel method based on time and frequency domain analysis is proposed for energy storage system (ESS) sizing, including both power sizing and energy sizing. According to the relationship between charge/discharge power and stored energy, the sizing model is established based on autocorrelation function and power spectral density (PSD) of the stochastic cycling process. The time and spectral characteristic of RE generation is analyzed based on the historical generation data of a wind farm and a PV station in the Northwest region of China. The size of energy storage is determined by the time and frequency domain method respectively. Comparing with the time domain method, it is showed that the frequency domain method is sufficient for energy storage sizing with enough accuracy and a much easier calculation process at the same time.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131655649","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 : 2016-10-01DOI: 10.1109/PMAPS.2016.7764212
Ming Wang, Yingmeng Xiang, Lingfeng Wang, Jie Jiang, Ruosong Xiao, K. Xie
The increasing load demand is pushing power system to operate near its limit, making it more vulnerable to various disturbances and attacks, especially those that might initiate cascading failures. In this study, the joint line-generation attack is introduced which assumes that the lines and generators can be tripped by malicious attacks simultaneously, and it is a natural extension of the previous node-only or line-only attacks. The joint line-generation attack strategy is explored based on a search space reduction algorithm. The simulation is conducted based on several representative test systems. The performance of the proposed attack strategy is compared with other attack strategies and the computational burden is analyzed. It is demonstrated that the proposed attack strategy is effective and computationally efficient. This work can provide some meaningful insight on how to prevent power system cascading failures initiated by joint attacks.
{"title":"Identification of critical line-generation combinations for hypothesized joint line-generation attacks","authors":"Ming Wang, Yingmeng Xiang, Lingfeng Wang, Jie Jiang, Ruosong Xiao, K. Xie","doi":"10.1109/PMAPS.2016.7764212","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764212","url":null,"abstract":"The increasing load demand is pushing power system to operate near its limit, making it more vulnerable to various disturbances and attacks, especially those that might initiate cascading failures. In this study, the joint line-generation attack is introduced which assumes that the lines and generators can be tripped by malicious attacks simultaneously, and it is a natural extension of the previous node-only or line-only attacks. The joint line-generation attack strategy is explored based on a search space reduction algorithm. The simulation is conducted based on several representative test systems. The performance of the proposed attack strategy is compared with other attack strategies and the computational burden is analyzed. It is demonstrated that the proposed attack strategy is effective and computationally efficient. This work can provide some meaningful insight on how to prevent power system cascading failures initiated by joint attacks.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132502131","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 : 2016-10-01DOI: 10.1109/PMAPS.2016.7764187
Meng Xu, C. Dent, Amy L. Wilson
Long-term generation investment (LTGI) models have been widely used as a decision-making tool of design of energy policy. Adequate LTGI models with detailed modelling of operations are often computationally intensive. Uncertainty involved in these models poses a great challenge to the uncertainty quantification in power system reliability. This paper presents a Bayesian framework for addressing this challenge systematically. The use of Bayesian techniques enables an efficient model calibration and quantitative study on the robustness of different market designs. In the case study on the future UK power system, the robustness index estimated by the calibrated model is obtained through uncertainty analysis of loss-of-load expectation.
{"title":"Uncertainty quantification in power system reliability using a Bayesian framework","authors":"Meng Xu, C. Dent, Amy L. Wilson","doi":"10.1109/PMAPS.2016.7764187","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764187","url":null,"abstract":"Long-term generation investment (LTGI) models have been widely used as a decision-making tool of design of energy policy. Adequate LTGI models with detailed modelling of operations are often computationally intensive. Uncertainty involved in these models poses a great challenge to the uncertainty quantification in power system reliability. This paper presents a Bayesian framework for addressing this challenge systematically. The use of Bayesian techniques enables an efficient model calibration and quantitative study on the robustness of different market designs. In the case study on the future UK power system, the robustness index estimated by the calibrated model is obtained through uncertainty analysis of loss-of-load expectation.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"383 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133433924","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 : 2016-10-01DOI: 10.1109/PMAPS.2016.7764218
Y. Liao, Yang Weng, Chin-Woo Tan, R. Rajagopal
The growing integration of distributed energy resources (DERs) in urban distribution grids raises various reliability issues due to complex uncertainties. With the large-scale penetration of DERs, traditional outage detection methods, which rely on customers making phone calls and smart meters' “last gasp” signals, will have limited performance because 1) the renewable generators can supply powers after line outages, and 2) many urban grids are mesh and line outages do not affect power supply. To address these drawbacks, we propose a new data-driven outage monitoring approach based on the stochastic time series analysis with the newly available smart meter data utilized. Specifically, based on the power flow analysis, we prove that the statistical dependency of time-series voltage measurements has significant changes after line outages. Hence, we use the optimal change point detection theory to detect and localize line outages. As the existing change point detection methods require the post-outage voltage distribution, which is unknown in power systems, we propose a maximum likelihood method to learn the distribution parameters from the historical data. The proposed outage detection using estimated parameters also achieves the optimal performance. Simulation results show highly accurate outage identification in IEEE standard distribution test systems with and without DERs using real smart meter data.
{"title":"Urban distribution grid line outage identification","authors":"Y. Liao, Yang Weng, Chin-Woo Tan, R. Rajagopal","doi":"10.1109/PMAPS.2016.7764218","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764218","url":null,"abstract":"The growing integration of distributed energy resources (DERs) in urban distribution grids raises various reliability issues due to complex uncertainties. With the large-scale penetration of DERs, traditional outage detection methods, which rely on customers making phone calls and smart meters' “last gasp” signals, will have limited performance because 1) the renewable generators can supply powers after line outages, and 2) many urban grids are mesh and line outages do not affect power supply. To address these drawbacks, we propose a new data-driven outage monitoring approach based on the stochastic time series analysis with the newly available smart meter data utilized. Specifically, based on the power flow analysis, we prove that the statistical dependency of time-series voltage measurements has significant changes after line outages. Hence, we use the optimal change point detection theory to detect and localize line outages. As the existing change point detection methods require the post-outage voltage distribution, which is unknown in power systems, we propose a maximum likelihood method to learn the distribution parameters from the historical data. The proposed outage detection using estimated parameters also achieves the optimal performance. Simulation results show highly accurate outage identification in IEEE standard distribution test systems with and without DERs using real smart meter data.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132789976","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 : 2016-10-01DOI: 10.1109/PMAPS.2016.7764070
M. Papic, I. Dobson
The paper presents an initial comparison of a transmission planning study of cascading outages with a statistical analysis of historical outages. The planning study identifies the most vulnerable places in the Idaho system and outages that lead to cascading and interruption of load. This analysis is based on a number of case scenarios (short-term and long-term) that cover different seasonal and operating conditions. The historical analysis processes Idaho outage data and estimates statistics, using the number of transmission line outages as a measure of the extent of cascading. An initial number of lines outaged can lead to a cascading propagation of further outages. How much line outages propagate is estimated from Idaho Power outage data. Also, the paper discusses some similarities in the results and highlights the different assumptions of the two approaches to cascading failure analysis.
{"title":"Comparing a transmission planning study of cascading with historical line outage data","authors":"M. Papic, I. Dobson","doi":"10.1109/PMAPS.2016.7764070","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764070","url":null,"abstract":"The paper presents an initial comparison of a transmission planning study of cascading outages with a statistical analysis of historical outages. The planning study identifies the most vulnerable places in the Idaho system and outages that lead to cascading and interruption of load. This analysis is based on a number of case scenarios (short-term and long-term) that cover different seasonal and operating conditions. The historical analysis processes Idaho outage data and estimates statistics, using the number of transmission line outages as a measure of the extent of cascading. An initial number of lines outaged can lead to a cascading propagation of further outages. How much line outages propagate is estimated from Idaho Power outage data. Also, the paper discusses some similarities in the results and highlights the different assumptions of the two approaches to cascading failure analysis.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115231965","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 : 2016-10-01DOI: 10.1109/PMAPS.2016.7764064
E. Scolari, D. Torregrossa, J.-Y. Le Boudec, M. Paolone
The paper describes a heuristic method for the ultra-short-term computation of prediction intervals (PIs) for photovoltaic (PV) power generation. The method allows for directly forecasting the AC active power output of a PV system by simply extracting information from past time series. Two main approaches are investigated. The former relies on experimentally observed correlations between the time derivative of the PV AC active power output and the errors caused by a generic point forecast technique. The latter approach represents an improvement of the first one, where the mentioned correlations are clustered as a function of the value of the AC active power. The work is framed in the context of microgrids and inertialess power systems control, where accounting for the fastest dynamics of the solar irradiance can become extremely valuable. We validate the proposed model using one month of AC active power measurements and for sub-second time horizons: 100, 250 and 500 ms.
{"title":"Ultra-short-term prediction intervals of photovoltaic AC active power","authors":"E. Scolari, D. Torregrossa, J.-Y. Le Boudec, M. Paolone","doi":"10.1109/PMAPS.2016.7764064","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764064","url":null,"abstract":"The paper describes a heuristic method for the ultra-short-term computation of prediction intervals (PIs) for photovoltaic (PV) power generation. The method allows for directly forecasting the AC active power output of a PV system by simply extracting information from past time series. Two main approaches are investigated. The former relies on experimentally observed correlations between the time derivative of the PV AC active power output and the errors caused by a generic point forecast technique. The latter approach represents an improvement of the first one, where the mentioned correlations are clustered as a function of the value of the AC active power. The work is framed in the context of microgrids and inertialess power systems control, where accounting for the fastest dynamics of the solar irradiance can become extremely valuable. We validate the proposed model using one month of AC active power measurements and for sub-second time horizons: 100, 250 and 500 ms.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"221 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115490666","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 : 2016-10-01DOI: 10.1109/PMAPS.2016.7764198
Fan Chen, Haitao Liu, Jun Li, Zheng Huang
The solution of optimal load curtailment for the selected system contingency states is the most important step for the reliability analysis of composite power system. The linear reactive remedial model considering the bus voltage and reactive power constrains was formulated first based on the decoupled AC load flow model. Aiming at dealing with the discrete control variables in the reactive power optimal problem, a hybrid optimal method combined with interior point method and Genetic Algorithm (GA) method is proposed. Some reliability indices are defined to represent the reactive power adequacy similar to the indices used for representing active power adequacy in this paper. Case studies have been carried out on the modified IEEE RTS to validate the proposed optimal algorithm and investigate the effect of discreteness of shunt compensation capacity and bus voltage on system reliability indices.
{"title":"Reactive power adequacy assessment of composite power system based on interior point method and genetic algorithm","authors":"Fan Chen, Haitao Liu, Jun Li, Zheng Huang","doi":"10.1109/PMAPS.2016.7764198","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764198","url":null,"abstract":"The solution of optimal load curtailment for the selected system contingency states is the most important step for the reliability analysis of composite power system. The linear reactive remedial model considering the bus voltage and reactive power constrains was formulated first based on the decoupled AC load flow model. Aiming at dealing with the discrete control variables in the reactive power optimal problem, a hybrid optimal method combined with interior point method and Genetic Algorithm (GA) method is proposed. Some reliability indices are defined to represent the reactive power adequacy similar to the indices used for representing active power adequacy in this paper. Case studies have been carried out on the modified IEEE RTS to validate the proposed optimal algorithm and investigate the effect of discreteness of shunt compensation capacity and bus voltage on system reliability indices.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121175386","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 : 2016-10-01DOI: 10.1109/PMAPS.2016.7763924
Wan Lingyun, Zhang Ying, Wei Tingting, Liao Yixi, Zhou Qing, Xia Lei, Wang Zhuding, Tang Fengying
For the reliability evaluation of overhead medium voltage distribution networks, the required data of traditional methods are too big to be collected and inputted. Moreover, some data of a distribution power grid, especially the planning distribution networks, cannot be completely provided. As a result, it is necessary to put forward simplified reliability evaluation formulae. In this paper, the simplified evaluation formulae of system average interruption duration and frequency indexes for failure outage and scheduled interruption are deduced respectively, considering the influences of not only the main lines, distribution transformers and switches of a single type, but also big lateral lines and various types of switches, thus making the formulae more practical. Moreover, according to the interruption times based on the line length or the line segment number, two sets of evaluation formulae of scheduled interruption are deduced. The reliability evaluation of IEEE RBTS-Bus2 is performed by using the deduced formulae, and the results of better precision are obtained with little increased input data.
{"title":"Simplified reliability evaluation formulae for overhead medium voltage distribution networks","authors":"Wan Lingyun, Zhang Ying, Wei Tingting, Liao Yixi, Zhou Qing, Xia Lei, Wang Zhuding, Tang Fengying","doi":"10.1109/PMAPS.2016.7763924","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7763924","url":null,"abstract":"For the reliability evaluation of overhead medium voltage distribution networks, the required data of traditional methods are too big to be collected and inputted. Moreover, some data of a distribution power grid, especially the planning distribution networks, cannot be completely provided. As a result, it is necessary to put forward simplified reliability evaluation formulae. In this paper, the simplified evaluation formulae of system average interruption duration and frequency indexes for failure outage and scheduled interruption are deduced respectively, considering the influences of not only the main lines, distribution transformers and switches of a single type, but also big lateral lines and various types of switches, thus making the formulae more practical. Moreover, according to the interruption times based on the line length or the line segment number, two sets of evaluation formulae of scheduled interruption are deduced. The reliability evaluation of IEEE RBTS-Bus2 is performed by using the deduced formulae, and the results of better precision are obtained with little increased input data.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124254427","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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