Pub Date : 2019-06-23DOI: 10.1109/PTC.2019.8810774
Keerachat Tantrapon, P. Jirapong, P. Thararak, Kannathat Mansuwan
Renewable energy sources, especially photovoltaic (PV), have grown significantly and become important sources for power generation in distribution systems. However, the PV power generation is fluctuated by cloud movement, weather conditions, and wind speed which directly affect the excessive operation of voltage regulation devices such as the on load tap changer (OLTC). This excessive operation will decrease the expected life cycle and increase maintenance requirements. This paper proposed an optional operation of the battery energy storage system (BESS) in microgrid by optimizing BESS active and reactive power with Cuckoo Search optimization (CSo). The main objective aims to minimize the OLTC tap operation under PV fluctuation. The CSO is implemented in MATLAB while DIgSILENT PowerFactory is used for load flow evaluation. Simulation case studies are performed using system data from the Mae Sa Riang microgrid in Thailand. Results show that the optimal operation of BESS using CSO can effectively reduce the number of OLTC tap operations in microgrid when compared to the results with the base case and microgrid controller.
可再生能源，特别是光伏(PV)，已成为配电系统中重要的发电来源。然而，光伏发电受云层运动、天气条件和风速的影响而波动，这直接影响到有载分接开关(OLTC)等电压调节装置的过度运行。这种过度的操作将缩短预期的生命周期并增加维护需求。本文采用杜鹃搜索优化(Cuckoo Search optimization, CSo)方法对电池储能系统(BESS)有功功率和无功功率进行优化，提出了电池储能系统(BESS)在微电网中的可选运行。主要目标是在PV波动下最小化OLTC抽头操作。CSO在MATLAB中实现，并使用DIgSILENT PowerFactory进行潮流评估。模拟案例研究使用泰国Mae Sa Riang微电网的系统数据进行。结果表明，与基本情况和微网控制器相比，基于CSO的BESS优化运行可以有效减少微网中OLTC分接操作的次数。
{"title":"Optimal Operation of Battery Energy Storage System in Smart Grid for Reducing Tap Changer Operation under Photovoltaic Fluctuation Using Cuckoo Search","authors":"Keerachat Tantrapon, P. Jirapong, P. Thararak, Kannathat Mansuwan","doi":"10.1109/PTC.2019.8810774","DOIUrl":"https://doi.org/10.1109/PTC.2019.8810774","url":null,"abstract":"Renewable energy sources, especially photovoltaic (PV), have grown significantly and become important sources for power generation in distribution systems. However, the PV power generation is fluctuated by cloud movement, weather conditions, and wind speed which directly affect the excessive operation of voltage regulation devices such as the on load tap changer (OLTC). This excessive operation will decrease the expected life cycle and increase maintenance requirements. This paper proposed an optional operation of the battery energy storage system (BESS) in microgrid by optimizing BESS active and reactive power with Cuckoo Search optimization (CSo). The main objective aims to minimize the OLTC tap operation under PV fluctuation. The CSO is implemented in MATLAB while DIgSILENT PowerFactory is used for load flow evaluation. Simulation case studies are performed using system data from the Mae Sa Riang microgrid in Thailand. Results show that the optimal operation of BESS using CSO can effectively reduce the number of OLTC tap operations in microgrid when compared to the results with the base case and microgrid controller.","PeriodicalId":187144,"journal":{"name":"2019 IEEE Milan PowerTech","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125184262","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 : 2019-06-23DOI: 10.1109/PTC.2019.8810750
J. Massignan, J. London, Carlos Dias Maciel, Michle Bessani, Vladimiro Miranda
Phasor Measurement Units (PMUs) in transmission systems is one of the most promising sources of data to increase situational awareness of network monitoring. However, the inclusion of PMU measurements along with the ones from traditional Supervisory Control and Data Acquisition (SCADA) systems to perform state estimation brings additional challenges, such as the vast difference in sampling rates and precision between these two types of measurements. This paper formally introduces a Bayesian inference approach in the form of a new State Estimator for transmission systems able to deal with the different sampling rates of those measurements. The proposed approach provides accurate state estimates even for buses that are not observable by PMU measurements, and when load variation occurs during the time interval between two SCADA data scans. Several simulation results (with IEEE transmission test systems) are used to illustrate the features of the proposed approach.
{"title":"PMUs and SCADA Measurements in Power System State Estimation through Bayesian Inference","authors":"J. Massignan, J. London, Carlos Dias Maciel, Michle Bessani, Vladimiro Miranda","doi":"10.1109/PTC.2019.8810750","DOIUrl":"https://doi.org/10.1109/PTC.2019.8810750","url":null,"abstract":"Phasor Measurement Units (PMUs) in transmission systems is one of the most promising sources of data to increase situational awareness of network monitoring. However, the inclusion of PMU measurements along with the ones from traditional Supervisory Control and Data Acquisition (SCADA) systems to perform state estimation brings additional challenges, such as the vast difference in sampling rates and precision between these two types of measurements. This paper formally introduces a Bayesian inference approach in the form of a new State Estimator for transmission systems able to deal with the different sampling rates of those measurements. The proposed approach provides accurate state estimates even for buses that are not observable by PMU measurements, and when load variation occurs during the time interval between two SCADA data scans. Several simulation results (with IEEE transmission test systems) are used to illustrate the features of the proposed approach.","PeriodicalId":187144,"journal":{"name":"2019 IEEE Milan PowerTech","volume":"460 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113989640","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 : 2019-06-23DOI: 10.1109/PTC.2019.8810793
Gustavo Aragón, Harsh Puri, Alexander Grass, S. Chala, C. Beecks
In this work, we introduce load prediction as continuous input for optimization models within an optimization framework for short-term control of complex energy systems. In this context, we investigated long short-term memory (LSTM) models for load prediction, because they allow incremental training in an application with continuous real-time data and have not been used in other works for continuous load prediction to our knowledge. The test and evaluation were realized using data sets of real residential data from different locations in different time resolution - hourly and minutely. Accordingly, we tested different recurrent neural network (RNN) parameters of the model such as the number of layers, the number of hidden nodes, the inclusion of regularization, and dropout in order to find the optimal LSTM configuration for our continuous load prediction application. Besides, we analyzed the quality of the LSTM algorithm by comparing it in continuous mode with the baseline model and in batch mode with the statistical model ARIMA. Training and prediction time, as well as the error stabilization time were parameters used for the evaluation. The results showed that LSTM algorithms are highly promising for integrating continuous load prediction with incremental learning.
{"title":"Incremental Deep-Learning for Continuous Load Prediction in Energy Management Systems","authors":"Gustavo Aragón, Harsh Puri, Alexander Grass, S. Chala, C. Beecks","doi":"10.1109/PTC.2019.8810793","DOIUrl":"https://doi.org/10.1109/PTC.2019.8810793","url":null,"abstract":"In this work, we introduce load prediction as continuous input for optimization models within an optimization framework for short-term control of complex energy systems. In this context, we investigated long short-term memory (LSTM) models for load prediction, because they allow incremental training in an application with continuous real-time data and have not been used in other works for continuous load prediction to our knowledge. The test and evaluation were realized using data sets of real residential data from different locations in different time resolution - hourly and minutely. Accordingly, we tested different recurrent neural network (RNN) parameters of the model such as the number of layers, the number of hidden nodes, the inclusion of regularization, and dropout in order to find the optimal LSTM configuration for our continuous load prediction application. Besides, we analyzed the quality of the LSTM algorithm by comparing it in continuous mode with the baseline model and in batch mode with the statistical model ARIMA. Training and prediction time, as well as the error stabilization time were parameters used for the evaluation. The results showed that LSTM algorithms are highly promising for integrating continuous load prediction with incremental learning.","PeriodicalId":187144,"journal":{"name":"2019 IEEE Milan PowerTech","volume":"316 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122786196","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 : 2019-06-23DOI: 10.1109/PTC.2019.8810580
Volker Scheffer, Hanko Ipach, C. Becker
With the increasing expansion of wind and solar power plants, these technologies will also have to contribute control reserve to guarantee frequency stability within the next couple of years. In order to maintain the security of supply at the same level in the future, it must be ensured that wind and solar power plants are able to feed in electricity into the distribution grid without bottlenecks when activated. The present work presents a grid state assessment, which takes into account the special features of the control reserve supply. The identification of a future grid state, which is necessary for an ex ante evaluation, poses the challenge of forecasting loads. The Boundary Load Flow method takes load uncertainties into account and is used to estimate a possible interval for all grid parameters. Grid congestions can thus be detected preventively and suppliers of control reserve can be approved or excluded. A validation in combination with an exemplary application shows the feasibility of the overall methodology.
{"title":"Distribution Grid State Assessment for Control Reserve Provision Using Boundary Load Flow","authors":"Volker Scheffer, Hanko Ipach, C. Becker","doi":"10.1109/PTC.2019.8810580","DOIUrl":"https://doi.org/10.1109/PTC.2019.8810580","url":null,"abstract":"With the increasing expansion of wind and solar power plants, these technologies will also have to contribute control reserve to guarantee frequency stability within the next couple of years. In order to maintain the security of supply at the same level in the future, it must be ensured that wind and solar power plants are able to feed in electricity into the distribution grid without bottlenecks when activated. The present work presents a grid state assessment, which takes into account the special features of the control reserve supply. The identification of a future grid state, which is necessary for an ex ante evaluation, poses the challenge of forecasting loads. The Boundary Load Flow method takes load uncertainties into account and is used to estimate a possible interval for all grid parameters. Grid congestions can thus be detected preventively and suppliers of control reserve can be approved or excluded. A validation in combination with an exemplary application shows the feasibility of the overall methodology.","PeriodicalId":187144,"journal":{"name":"2019 IEEE Milan PowerTech","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134125275","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 : 2019-06-23DOI: 10.1109/PTC.2019.8810437
M. Franken, Hans Barrios, Alexander B. Schrief, R. Puffer
This paper presents an extended mixed integer linear programming (MILP) formulation of the transmission expansion problem considering a detailed modeling of expansion costs. In contrast to the widely used formulation, using cost-per-km coefficients multiplied with the circuit length, here costs are differentiated in costs for conductors, switching bays and poles. This allows a more accurate consideration of transmission corridors carrying multiple parallel circuits, since expansion costs contain both circuit- and corridor-specific costs. Exemplary results show a significant impact of the detailed consideration of expansion costs on the identified expansion measures proving the necessity of a more detailed consideration of expansion costs.
{"title":"Transmission Expansion Planning Considering Detailed Modeling of Expansion Costs","authors":"M. Franken, Hans Barrios, Alexander B. Schrief, R. Puffer","doi":"10.1109/PTC.2019.8810437","DOIUrl":"https://doi.org/10.1109/PTC.2019.8810437","url":null,"abstract":"This paper presents an extended mixed integer linear programming (MILP) formulation of the transmission expansion problem considering a detailed modeling of expansion costs. In contrast to the widely used formulation, using cost-per-km coefficients multiplied with the circuit length, here costs are differentiated in costs for conductors, switching bays and poles. This allows a more accurate consideration of transmission corridors carrying multiple parallel circuits, since expansion costs contain both circuit- and corridor-specific costs. Exemplary results show a significant impact of the detailed consideration of expansion costs on the identified expansion measures proving the necessity of a more detailed consideration of expansion costs.","PeriodicalId":187144,"journal":{"name":"2019 IEEE Milan PowerTech","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133832830","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 : 2019-06-23DOI: 10.1109/PTC.2019.8810901
Angie D.Vasquez, T. Sousa
This work is focused in analyzing the static voltage stability of great power systems considering a parallel processing approach. The analysis is based on determining the critical power point will lead to the system voltage collapse. In this sense, the Continuation Power Flow (CPF) is applied to Transmission Systems and to the Distribution Networks, considering the insertion of Wind Energy Conversion Systems (WECS), the Cespedes Method is used. To these simulation processes, a parallel computing technology that allows its execution in different processing units simultaneously is proposed, reducing the total execution time. So, the Graphics Processing Units (GPU) is applied to the intensive computational calculations and the CPU is applied in the sequence of the algorithm and to perform smaller calculations. To validate the proposed approach some tests is presented to compare computational time of the GPU+CPU (heterogeneous environment) and CPU (serial way) modes.
{"title":"A Parallel Processing Approach to Stability Analysis Considering Transmission and Distribution Systems","authors":"Angie D.Vasquez, T. Sousa","doi":"10.1109/PTC.2019.8810901","DOIUrl":"https://doi.org/10.1109/PTC.2019.8810901","url":null,"abstract":"This work is focused in analyzing the static voltage stability of great power systems considering a parallel processing approach. The analysis is based on determining the critical power point will lead to the system voltage collapse. In this sense, the Continuation Power Flow (CPF) is applied to Transmission Systems and to the Distribution Networks, considering the insertion of Wind Energy Conversion Systems (WECS), the Cespedes Method is used. To these simulation processes, a parallel computing technology that allows its execution in different processing units simultaneously is proposed, reducing the total execution time. So, the Graphics Processing Units (GPU) is applied to the intensive computational calculations and the CPU is applied in the sequence of the algorithm and to perform smaller calculations. To validate the proposed approach some tests is presented to compare computational time of the GPU+CPU (heterogeneous environment) and CPU (serial way) modes.","PeriodicalId":187144,"journal":{"name":"2019 IEEE Milan PowerTech","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115116854","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 : 2019-06-23DOI: 10.1109/PTC.2019.8810458
M. Nizami, M. J. Hossain, B. M. R. Amin, M. Kashif, Edstan Fernandez, K. Mahmud
In a transactive energy (TE) framework, prosumers can participate in peer-to-peer (P2P) energy trading with neighbors. TE also allows prosumers to participate in grid services by trading their excess energy or energy consumption flexibility with the grid operators, energy suppliers, and third-party energy companies (e.g., Aggregators). This paper presents a novel bidding strategy for small-scale residential prosumers for energy trading in the day-ahead TE market using the flexibilities of residential battery energy storage systems to maximize the profit from energy trading. The bidding model is formulated as a bi-level optimization problem that determines energy trading bids to maximize profits for the prosumer in the upper level, while the lower-level problem schedules the operation of residential storage units with respect to minimum storage degradation and optimum user comfort. A comprehensive storage model is developed that incorporates the operational constraints and the degradation of storage units when they undergo frequent charge-discharge cycles for the energy trading. The proposed bidding model is evaluated via a case study for a typical Australian prosumer and results indicate the efficacy of the proposed model in terms of profit maximization for the prosumer while satisfying user preferences and constraints related to the operation of the storage units.
{"title":"Transactive Energy Trading of Residential Prosumers Using Battery Energy Storage Systems","authors":"M. Nizami, M. J. Hossain, B. M. R. Amin, M. Kashif, Edstan Fernandez, K. Mahmud","doi":"10.1109/PTC.2019.8810458","DOIUrl":"https://doi.org/10.1109/PTC.2019.8810458","url":null,"abstract":"In a transactive energy (TE) framework, prosumers can participate in peer-to-peer (P2P) energy trading with neighbors. TE also allows prosumers to participate in grid services by trading their excess energy or energy consumption flexibility with the grid operators, energy suppliers, and third-party energy companies (e.g., Aggregators). This paper presents a novel bidding strategy for small-scale residential prosumers for energy trading in the day-ahead TE market using the flexibilities of residential battery energy storage systems to maximize the profit from energy trading. The bidding model is formulated as a bi-level optimization problem that determines energy trading bids to maximize profits for the prosumer in the upper level, while the lower-level problem schedules the operation of residential storage units with respect to minimum storage degradation and optimum user comfort. A comprehensive storage model is developed that incorporates the operational constraints and the degradation of storage units when they undergo frequent charge-discharge cycles for the energy trading. The proposed bidding model is evaluated via a case study for a typical Australian prosumer and results indicate the efficacy of the proposed model in terms of profit maximization for the prosumer while satisfying user preferences and constraints related to the operation of the storage units.","PeriodicalId":187144,"journal":{"name":"2019 IEEE Milan PowerTech","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115713619","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 : 2019-06-23DOI: 10.1109/PTC.2019.8810511
I. Daminov, A. Prokhorov, R. Caire, M. Alvarez‐Herault
This paper proposes algorithm, defining the dynamic transformer rating (DTR) for maximization of energy transfer through oil-immersed transformer. Algorithm ensures that windings temperature and loss of insulation life do not exceed their permissible limits. To achieve this goal, we use receding horizon control. Receding horizon control considers load and ambient temperature at past and future intervals to update the DTR. Proposed algorithm is intended for application in real-time economic dispatch at balancing market where it could allow the decreasing of energy generation cost. We consider a two-machine power system as case study, where cheap generation is constrained by transformer rating. The expensive generation does not have any network constraints. The algorithm application increased the cheap generation by 12% in comparison with static thermal limit and by 3% in comparison with static thermal limit corrected to ambient temperature. The generation rescheduling, allowed by DTR, decreased the energy generation cost by 27.9% and 9.8% correspondingly.
{"title":"Receding horizon algorithm for dynamic transformer rating and its application for real-time economic dispatch","authors":"I. Daminov, A. Prokhorov, R. Caire, M. Alvarez‐Herault","doi":"10.1109/PTC.2019.8810511","DOIUrl":"https://doi.org/10.1109/PTC.2019.8810511","url":null,"abstract":"This paper proposes algorithm, defining the dynamic transformer rating (DTR) for maximization of energy transfer through oil-immersed transformer. Algorithm ensures that windings temperature and loss of insulation life do not exceed their permissible limits. To achieve this goal, we use receding horizon control. Receding horizon control considers load and ambient temperature at past and future intervals to update the DTR. Proposed algorithm is intended for application in real-time economic dispatch at balancing market where it could allow the decreasing of energy generation cost. We consider a two-machine power system as case study, where cheap generation is constrained by transformer rating. The expensive generation does not have any network constraints. The algorithm application increased the cheap generation by 12% in comparison with static thermal limit and by 3% in comparison with static thermal limit corrected to ambient temperature. The generation rescheduling, allowed by DTR, decreased the energy generation cost by 27.9% and 9.8% correspondingly.","PeriodicalId":187144,"journal":{"name":"2019 IEEE Milan PowerTech","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122029781","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 : 2019-06-23DOI: 10.1109/PTC.2019.8810732
P. Faria, João Spínola, Z. Vale
The actual context for smart grid implementation implies the development of tools to support the diverse player’s decisions. The present paper addresses a multi-period consumer’s management methodology for the scheduling of demand flexibility initiatives and on-site generation. The objective is to minimize the energy costs for the consumer, taking into account his resources. The paper also considers the use of dynamic pricing with the intent of studying its effect on load shifting schedule. The results obtained show how the consumers can use this methodology to achieve new efficiency levels regarding their energy use, and therefore costs.
{"title":"Modeling of Consumer Preferences and Constraints for the Optimal Schedule of Consumption Shifting","authors":"P. Faria, João Spínola, Z. Vale","doi":"10.1109/PTC.2019.8810732","DOIUrl":"https://doi.org/10.1109/PTC.2019.8810732","url":null,"abstract":"The actual context for smart grid implementation implies the development of tools to support the diverse player’s decisions. The present paper addresses a multi-period consumer’s management methodology for the scheduling of demand flexibility initiatives and on-site generation. The objective is to minimize the energy costs for the consumer, taking into account his resources. The paper also considers the use of dynamic pricing with the intent of studying its effect on load shifting schedule. The results obtained show how the consumers can use this methodology to achieve new efficiency levels regarding their energy use, and therefore costs.","PeriodicalId":187144,"journal":{"name":"2019 IEEE Milan PowerTech","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125060418","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 : 2019-06-23DOI: 10.1109/PTC.2019.8810845
Erlend Sandø Kiel, G. Kjølle
Blackouts in the power system are rare events that can have large consequences for society. Successful preparation and prevention of such events calls for models capable of predicting their occurrence. The simultaneous outage of multiple components is of special interest in an N-l secure transmission grid. Spatio-temporal correlation in probability of failure for components can cause blackouts to occur more often than anticipated. This paper demonstrates a new method of calculating time-series of component unavailability due to external threats based on historical data. The time-series of unavailability can be used to predict the expected occurrence of contingencies throughout the year. A test case is presented where an hourly time series of wind dependent failure probabilities and historical outage durations of transmission lines are combined to illustrate the proposed method. The results show that the simultaneous unavailability of multiple transmission lines may be significantly larger than estimated using traditional reliability analysis.
{"title":"Transmission line unavailability due to correlated threat exposure","authors":"Erlend Sandø Kiel, G. Kjølle","doi":"10.1109/PTC.2019.8810845","DOIUrl":"https://doi.org/10.1109/PTC.2019.8810845","url":null,"abstract":"Blackouts in the power system are rare events that can have large consequences for society. Successful preparation and prevention of such events calls for models capable of predicting their occurrence. The simultaneous outage of multiple components is of special interest in an N-l secure transmission grid. Spatio-temporal correlation in probability of failure for components can cause blackouts to occur more often than anticipated. This paper demonstrates a new method of calculating time-series of component unavailability due to external threats based on historical data. The time-series of unavailability can be used to predict the expected occurrence of contingencies throughout the year. A test case is presented where an hourly time series of wind dependent failure probabilities and historical outage durations of transmission lines are combined to illustrate the proposed method. The results show that the simultaneous unavailability of multiple transmission lines may be significantly larger than estimated using traditional reliability analysis.","PeriodicalId":187144,"journal":{"name":"2019 IEEE Milan PowerTech","volume":"493 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123401439","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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