Pub Date : 2016-10-27DOI: 10.1109/PMAPS.2016.7763931
P. Mazidi, Mian Du, Lina Bertling Tjernberg, M. A. Sanz Bobi
In this paper, a data driven framework for performance and maintenance evaluation (PAME) of wind turbines (WT) is proposed. To develop the framework, SCADA data of WTs are adopted and several parameters are carefully selected to create a normal behavior model. This model which is based on Neural Networks estimates operation of WT and aberrations are collected as deviations. Afterwards, in order to capture patterns of deviations, self-organizing map is applied to cluster the deviations. From investigations on deviations and clustering results, a time-discrete finite state space Markov chain is built for mid-term operation and maintenance evaluation. With the purpose of performance and maintenance assessment, two anomaly indexes are defined and mathematically formulated. Moreover, Production Loss Profit is defined for Preventive Maintenance efficiency assessment. By comparing the indexes calculated for 9 WTs, current performance and maintenance strategies can be evaluated, and results demonstrate capability and effectiveness of the proposed framework.
{"title":"A performance and maintenance evaluation framework for wind turbines","authors":"P. Mazidi, Mian Du, Lina Bertling Tjernberg, M. A. Sanz Bobi","doi":"10.1109/PMAPS.2016.7763931","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7763931","url":null,"abstract":"In this paper, a data driven framework for performance and maintenance evaluation (PAME) of wind turbines (WT) is proposed. To develop the framework, SCADA data of WTs are adopted and several parameters are carefully selected to create a normal behavior model. This model which is based on Neural Networks estimates operation of WT and aberrations are collected as deviations. Afterwards, in order to capture patterns of deviations, self-organizing map is applied to cluster the deviations. From investigations on deviations and clustering results, a time-discrete finite state space Markov chain is built for mid-term operation and maintenance evaluation. With the purpose of performance and maintenance assessment, two anomaly indexes are defined and mathematically formulated. Moreover, Production Loss Profit is defined for Preventive Maintenance efficiency assessment. By comparing the indexes calculated for 9 WTs, current performance and maintenance strategies can be evaluated, and results demonstrate capability and effectiveness of the proposed framework.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128259059","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 : 2016-10-16DOI: 10.1109/PMAPS.2016.7764088
Mingyang Sun, I. Konstantelos, G. Strbac
The increasing penetration of intermittent energy sources along with the introduction of shiftable load elements renders transmission network expansion planning (TNEP) a challenging task. In particular, the ever-expanding spectrum of possible operating points necessitates the consideration of a very large number of scenarios within a cost-benefit framework, leading to computational issues. On the other hand, failure to adequately capture the behavior of stochastic parameters can lead to inefficient expansion plans. This paper proposes a novel TNEP framework that accommodates multiple sources of operational stochasticity. Inter-spatial dependencies between loads in various locations and intermittent generation units' output are captured by using a multivariate Gaussian copula. This statistical model forms the basis of a Monte Carlo analysis framework for exploring the uncertainty state-space. Benders decomposition is applied to efficiently split the investment and operation problems. The advantages of the proposed model are demonstrated through a case study on the IEEE 118-bus system. By evaluating the confidence interval of the optimality gap, the advantages of the proposed approach over conventional techniques are clearly demonstrated.
{"title":"Transmission network expansion planning with stochastic multivariate load and wind modeling","authors":"Mingyang Sun, I. Konstantelos, G. Strbac","doi":"10.1109/PMAPS.2016.7764088","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764088","url":null,"abstract":"The increasing penetration of intermittent energy sources along with the introduction of shiftable load elements renders transmission network expansion planning (TNEP) a challenging task. In particular, the ever-expanding spectrum of possible operating points necessitates the consideration of a very large number of scenarios within a cost-benefit framework, leading to computational issues. On the other hand, failure to adequately capture the behavior of stochastic parameters can lead to inefficient expansion plans. This paper proposes a novel TNEP framework that accommodates multiple sources of operational stochasticity. Inter-spatial dependencies between loads in various locations and intermittent generation units' output are captured by using a multivariate Gaussian copula. This statistical model forms the basis of a Monte Carlo analysis framework for exploring the uncertainty state-space. Benders decomposition is applied to efficiently split the investment and operation problems. The advantages of the proposed model are demonstrated through a case study on the IEEE 118-bus system. By evaluating the confidence interval of the optimality gap, the advantages of the proposed approach over conventional techniques are clearly demonstrated.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131279701","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.2016.7764105
P. Zhou, Zi-Yuan Chen, Hongqin Yang, Lili Wen, Yin Liu, Bo Hu, Yinghao Ma, Yun Xia, Ruosong Xiao, Bo Li
The switching in of renewable resources to micro-grids and the implementation of demand response strategy has made reliability evaluation of micro-grid become increasingly complex. This paper focuses on the influence of demand response strategy on micro-grid's reliability. The coordination degree between the micro-grid's new energies and loads can influence micro-grid's reliability and the utilization rate of new resources. Concerning this problem, a load demand response model based on the degree to which the micro-grid's new energies satisfy the load is built. The index of the satisfaction degree is defined. Based on the time period effect of the photovoltaic generating set's contribution and the TOU electricity price strategy, a load demand response model to achieve the maximum satisfaction degree is set up, and is solved by the PSO algorithm. According to the rectified load curve and considering the micro-grid's structure as well as the energy-storage equipment's contribution strategy, a method to evaluate reliability of the grid-connected micro-grid considering demand response is established. The results of the calculation example indicate that the proposed load demand response strategy based on the degree to which the micro-grid's new energies satisfy the load can efficiently improve the reliability of micro-grid.
{"title":"Reliability evaluation of grid-connected micro-grid considering demand response","authors":"P. Zhou, Zi-Yuan Chen, Hongqin Yang, Lili Wen, Yin Liu, Bo Hu, Yinghao Ma, Yun Xia, Ruosong Xiao, Bo Li","doi":"10.1109/PMAPS.2016.7764105","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764105","url":null,"abstract":"The switching in of renewable resources to micro-grids and the implementation of demand response strategy has made reliability evaluation of micro-grid become increasingly complex. This paper focuses on the influence of demand response strategy on micro-grid's reliability. The coordination degree between the micro-grid's new energies and loads can influence micro-grid's reliability and the utilization rate of new resources. Concerning this problem, a load demand response model based on the degree to which the micro-grid's new energies satisfy the load is built. The index of the satisfaction degree is defined. Based on the time period effect of the photovoltaic generating set's contribution and the TOU electricity price strategy, a load demand response model to achieve the maximum satisfaction degree is set up, and is solved by the PSO algorithm. According to the rectified load curve and considering the micro-grid's structure as well as the energy-storage equipment's contribution strategy, a method to evaluate reliability of the grid-connected micro-grid considering demand response is established. The results of the calculation example indicate that the proposed load demand response strategy based on the degree to which the micro-grid's new energies satisfy the load can efficiently improve the reliability of micro-grid.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"47 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":"115388867","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.2016.7764101
Cai Guang-lin, Lin Yong, Hu Jiajia, Chen Ya, H. Bo, L. Bo
With the energy crisis and environment problem increasing prominently, as a new means of transportation, electric vehicles (EV, Electric Vehicle) will be accessed to distribution networks more and more, leading to a huge challenge on power system reliability. This paper mainly studies the reliability evaluation of medium voltage distribution network with private EV accessed. First based on the residents commuting patterns, the impact of private EV charging and discharging characteristics on the load profile is analyzed quantitatively. Based on load zoning, the optimal driving route model with the objective function of the shortest path and shortest time is proposed, which is then solved by the dynamic programming algorithm. Finally, by considering EV charging and discharging characteristics, spatial distribution characteristics, EV charging and discharging model is proposed. Then by studying the impact of distribution system failures during EV charging and discharging process, novel reliability indices are defined. Based on the considerations above, Monte Carlo simulation method based on Latin Hypercube Sampling is applied for reliability evaluation of medium voltage distribution system considering private EV accessed. A modified IEEE-RBTS Bus2 system is used as an example to analyze the impact of EV accessed ratio and EV charging mode on distribution system reliability, verifying the feasibility and effectiveness of the model and method proposed.
{"title":"Reliability evaluation of medium voltage distribution network with private electric vehicle","authors":"Cai Guang-lin, Lin Yong, Hu Jiajia, Chen Ya, H. Bo, L. Bo","doi":"10.1109/PMAPS.2016.7764101","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764101","url":null,"abstract":"With the energy crisis and environment problem increasing prominently, as a new means of transportation, electric vehicles (EV, Electric Vehicle) will be accessed to distribution networks more and more, leading to a huge challenge on power system reliability. This paper mainly studies the reliability evaluation of medium voltage distribution network with private EV accessed. First based on the residents commuting patterns, the impact of private EV charging and discharging characteristics on the load profile is analyzed quantitatively. Based on load zoning, the optimal driving route model with the objective function of the shortest path and shortest time is proposed, which is then solved by the dynamic programming algorithm. Finally, by considering EV charging and discharging characteristics, spatial distribution characteristics, EV charging and discharging model is proposed. Then by studying the impact of distribution system failures during EV charging and discharging process, novel reliability indices are defined. Based on the considerations above, Monte Carlo simulation method based on Latin Hypercube Sampling is applied for reliability evaluation of medium voltage distribution system considering private EV accessed. A modified IEEE-RBTS Bus2 system is used as an example to analyze the impact of EV accessed ratio and EV charging mode on distribution system reliability, verifying the feasibility and effectiveness of the model and method proposed.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"10 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":"115481427","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.2016.7764055
H. Rui, W. Wellssow
Deregulation and the massive growth of Distributed Generation (DG) in distribution networks are forcing the network operators to improve the operational efficiency and power quality at the distribution level. Distribution Automation (DA) enables operators to monitor and control various distribution system components and thus decrease the restoration time after a fault occurs. To implement DA properly, it is necessary to integrate communication and information systems and automation devices. In this paper, DA measures are modeled based on the supply restoration process. Different levels of automation are assessed by the Smart Grid Metrics (SGM) approach with respect to reliability improvement [1]. Several traditional grid concepts are modeled and compared with DA options. An analytical Probabilistic Reliability Calculation (PRC) approach is applied to calculate the reliability indices to quantify the benefits. Finally, a cost/benefit analysis is carried out in order to assess DA from both a technical and an economic point of view.
{"title":"A smart grid metrics assessment of distribution automation for reliability improvement","authors":"H. Rui, W. Wellssow","doi":"10.1109/PMAPS.2016.7764055","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764055","url":null,"abstract":"Deregulation and the massive growth of Distributed Generation (DG) in distribution networks are forcing the network operators to improve the operational efficiency and power quality at the distribution level. Distribution Automation (DA) enables operators to monitor and control various distribution system components and thus decrease the restoration time after a fault occurs. To implement DA properly, it is necessary to integrate communication and information systems and automation devices. In this paper, DA measures are modeled based on the supply restoration process. Different levels of automation are assessed by the Smart Grid Metrics (SGM) approach with respect to reliability improvement [1]. Several traditional grid concepts are modeled and compared with DA options. An analytical Probabilistic Reliability Calculation (PRC) approach is applied to calculate the reliability indices to quantify the benefits. Finally, a cost/benefit analysis is carried out in order to assess DA from both a technical and an economic point of view.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"26 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":"127252507","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.2016.7764216
Can Bikcora, N. Refa, L. Verheijen, S. Weiland
To enable better smart charging solutions, this paper investigates the day-ahead probabilistic forecasting of the availability and the charging rate at charging stations for plug-in electric vehicles. Generalized linear models with logistic link functions are at the core of both forecast scenarios. Moreover, the availability forecast at a charging point is simply a binomial problem, whereas the charging rate forecast is handled via an ordered logistic model after categorizing the feasible range of values. These two scenarios are evaluated on real data collected from two representatives of the most occupied charging points in the Netherlands, with the focus of the analysis kept at the selection of essential regressors. Based on the ranked probability scores associated with the day-ahead forecasts generated for the last nine months of 2015, it is concluded that the usefulness of predictive models depends highly on the charging station. When contributing substantially to performance, such models possess a simple structure with a few basic lagged and indicator variables.
{"title":"Prediction of availability and charging rate at charging stations for electric vehicles","authors":"Can Bikcora, N. Refa, L. Verheijen, S. Weiland","doi":"10.1109/PMAPS.2016.7764216","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764216","url":null,"abstract":"To enable better smart charging solutions, this paper investigates the day-ahead probabilistic forecasting of the availability and the charging rate at charging stations for plug-in electric vehicles. Generalized linear models with logistic link functions are at the core of both forecast scenarios. Moreover, the availability forecast at a charging point is simply a binomial problem, whereas the charging rate forecast is handled via an ordered logistic model after categorizing the feasible range of values. These two scenarios are evaluated on real data collected from two representatives of the most occupied charging points in the Netherlands, with the focus of the analysis kept at the selection of essential regressors. Based on the ranked probability scores associated with the day-ahead forecasts generated for the last nine months of 2015, it is concluded that the usefulness of predictive models depends highly on the charging station. When contributing substantially to performance, such models possess a simple structure with a few basic lagged and indicator variables.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"32 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":"126111273","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.2016.7764182
K. A. Agyeman, Sekyung Han, Ryo Umezawa
In this work, we provide a completely new approach for a short-term power system reliability that can be utilized for system operation planning. The proposed index incorporates the system frequency as a reliability of the grid and its deviation over a short time period. Although frequency performance is often employed as a quality metric for the past system operation, our model can address the future system condition by forecasting the statistics of the system frequency for a specific operation condition. In our model, demand, renewable source, battery and generators are stochastically incorporated. Using the equilibrium of demand and supply, and the physical constraints of automatic generation control (AGC), a model is developed, from which the system frequency distribution is obtained. From the contrived system frequency, statistical function, referred to herein as Frequency Reliability Distribution Function (FRDF), is proposed from which various criteria could be developed for short-term reliability. The developed FRDF along with the pertaining metrics are utilized for some case studies with IEEE reliability test system.
{"title":"A new approach for frequency based short-term reliability for a power system","authors":"K. A. Agyeman, Sekyung Han, Ryo Umezawa","doi":"10.1109/PMAPS.2016.7764182","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764182","url":null,"abstract":"In this work, we provide a completely new approach for a short-term power system reliability that can be utilized for system operation planning. The proposed index incorporates the system frequency as a reliability of the grid and its deviation over a short time period. Although frequency performance is often employed as a quality metric for the past system operation, our model can address the future system condition by forecasting the statistics of the system frequency for a specific operation condition. In our model, demand, renewable source, battery and generators are stochastically incorporated. Using the equilibrium of demand and supply, and the physical constraints of automatic generation control (AGC), a model is developed, from which the system frequency distribution is obtained. From the contrived system frequency, statistical function, referred to herein as Frequency Reliability Distribution Function (FRDF), is proposed from which various criteria could be developed for short-term reliability. The developed FRDF along with the pertaining metrics are utilized for some case studies with IEEE reliability test system.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"77 12 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":"122840339","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.2016.7764056
R. Wang, Jie Wang, Xiao Mi
Power system is a nonlinear network with many stochastic disturbances. Recently, with large-scale renewable power integration such as wind power and solar energy, it has caused much more stochastic factors in the power system. The traditional deterministic linear analysis methods need to be improved. Therefore, it has important value to use nonlinear differential equations to analyze the stochastic small disturbance stability of multi-machine system. In view of high dimensional and nonlinear factors in the multi-generator system, the nonlinear system model with random disturbances is applied in the paper and combined with the energy function method to analyze the power system stochastic small disturbance stability. The stability problem can be simplified from the complex high-dimensional vector problem into the simple one-dimensional vector problem by using energy function. Moreover, the Monte Carlo method is used to analyze the stability probability of the multi-machine system which is subject to random disturbances. The 4 machine 11 bus system is chosen as an example and the system stability probabilities under different stochastic disturbances can be got by simulation. By analyzing the simulation results, the practicality and validity of this analysis method is verified.
{"title":"Stochastic small disturbance stability analysis of multi-machine system based on energy function","authors":"R. Wang, Jie Wang, Xiao Mi","doi":"10.1109/PMAPS.2016.7764056","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764056","url":null,"abstract":"Power system is a nonlinear network with many stochastic disturbances. Recently, with large-scale renewable power integration such as wind power and solar energy, it has caused much more stochastic factors in the power system. The traditional deterministic linear analysis methods need to be improved. Therefore, it has important value to use nonlinear differential equations to analyze the stochastic small disturbance stability of multi-machine system. In view of high dimensional and nonlinear factors in the multi-generator system, the nonlinear system model with random disturbances is applied in the paper and combined with the energy function method to analyze the power system stochastic small disturbance stability. The stability problem can be simplified from the complex high-dimensional vector problem into the simple one-dimensional vector problem by using energy function. Moreover, the Monte Carlo method is used to analyze the stability probability of the multi-machine system which is subject to random disturbances. The 4 machine 11 bus system is chosen as an example and the system stability probabilities under different stochastic disturbances can be got by simulation. By analyzing the simulation results, the practicality and validity of this analysis method is verified.","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":"123993882","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.2016.7764193
Lan Luo, Xia Zhao, Xinyi Li, Wei Yan, Guoping Liu, P. Zhou, Lili Wen
Uncertainties in frequency regulations (FR) of generators and loads, and their effects on probabilistic power flow (PPF) analysis are addressed in this paper. The conventional PPF analysis is based on power flow model, without considering the frequency regulation. However, according to frequency regulation characteristics, generation outputs and load demand will respond to frequency deviation. Furthermore, since owners of generator units have strong economic motivations to prevent effective governing response as expected, and the load-frequency regulation coefficient of load varies with its components, the overall regulation coefficients of system cannot be completely determined and thus have some uncertainties. With some assumptions on the probabilistic distributions of regulation coefficients of generators and loads, a probabilistic power flow problem considering uncertainties in frequency regulation is presented and then solved by point estimate method (PEM). Simulations results from the IEEE-9 bus system and a 173-bus in real life with and without uncertainties in frequency regulation coefficients considered are presented and compared.
{"title":"Effects of uncertainties in frequency regulations on probabilistic power flow analysis","authors":"Lan Luo, Xia Zhao, Xinyi Li, Wei Yan, Guoping Liu, P. Zhou, Lili Wen","doi":"10.1109/PMAPS.2016.7764193","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764193","url":null,"abstract":"Uncertainties in frequency regulations (FR) of generators and loads, and their effects on probabilistic power flow (PPF) analysis are addressed in this paper. The conventional PPF analysis is based on power flow model, without considering the frequency regulation. However, according to frequency regulation characteristics, generation outputs and load demand will respond to frequency deviation. Furthermore, since owners of generator units have strong economic motivations to prevent effective governing response as expected, and the load-frequency regulation coefficient of load varies with its components, the overall regulation coefficients of system cannot be completely determined and thus have some uncertainties. With some assumptions on the probabilistic distributions of regulation coefficients of generators and loads, a probabilistic power flow problem considering uncertainties in frequency regulation is presented and then solved by point estimate method (PEM). Simulations results from the IEEE-9 bus system and a 173-bus in real life with and without uncertainties in frequency regulation coefficients considered are presented and compared.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"21 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":"114854831","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.2016.7764120
Yang Zhichun, Shen Yu, Yang Fan, Wang Zilin, Zhang Jun, Wang Dongxu, Cai Wei
Batteries have been generally adopted as energy storage component at distribution automation terminal, however bad operating environment have a great impact on the performance and service life, which is very difficult for operation and maintenance of batteries. Online monitoring and state diagnosis technology is developed, through acquisition battery real time voltage, current and temperature, use of the existing communication network of distribution automation (such as optical fiber, wireless and so on) uploaded to the battery online monitoring and state diagnosis platform; battery state diagnosis model is established using neural network based on the Unscented Kalman filter (UKF), which through battery voltage, current and temperature estimation of SOC real time value; an reasonable plan is given by online monitoring and state diagnosis platform according to SOC real time value, which provide technical basis for state-based maintenance of battery.
{"title":"Research on online monitoring and state diagnosis of battery for distribution automation","authors":"Yang Zhichun, Shen Yu, Yang Fan, Wang Zilin, Zhang Jun, Wang Dongxu, Cai Wei","doi":"10.1109/PMAPS.2016.7764120","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764120","url":null,"abstract":"Batteries have been generally adopted as energy storage component at distribution automation terminal, however bad operating environment have a great impact on the performance and service life, which is very difficult for operation and maintenance of batteries. Online monitoring and state diagnosis technology is developed, through acquisition battery real time voltage, current and temperature, use of the existing communication network of distribution automation (such as optical fiber, wireless and so on) uploaded to the battery online monitoring and state diagnosis platform; battery state diagnosis model is established using neural network based on the Unscented Kalman filter (UKF), which through battery voltage, current and temperature estimation of SOC real time value; an reasonable plan is given by online monitoring and state diagnosis platform according to SOC real time value, which provide technical basis for state-based maintenance of battery.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"28 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":"124059540","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：