Pub Date : 2016-10-01DOI: 10.1109/PMAPS.2016.7764117
Shenghu Li, Zhuang Qian, Xiaoyan Zhang
Probabilistic short-circuit analysis (PSCA) determines vulnerability of the transmission systems. The failure uncertainty and fluctuating wind power add difficulty to PSCA. The pre-fault system states are derived by simultaneous solution to steady state constraints of power system and the doubly-fed induction generators (DFIGs). A hybrid probabilistic simulation is newly proposed, with the fault branches enumerated and probabilistically weighted, while the fault parameters sampled. The variance coefficient of hybrid Monte-Carlo (HMC) simulation is defined to describe the convergence, which is speeded up by the optimal HMC (OPHMC) with the density function of the fault types. The numerical analysis of IEEE RTS system shows the impacts of high-order fault and wind power by comparing expectation, variance, and distribution of the bus voltage and branch current. The accuracy, convergence, efficiency of Monte-Carlo (MC), HMC and OPHMC methods are compared.
{"title":"Probabilistic short-circuit analysis of wind power system based on sampling with optimal density function","authors":"Shenghu Li, Zhuang Qian, Xiaoyan Zhang","doi":"10.1109/PMAPS.2016.7764117","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764117","url":null,"abstract":"Probabilistic short-circuit analysis (PSCA) determines vulnerability of the transmission systems. The failure uncertainty and fluctuating wind power add difficulty to PSCA. The pre-fault system states are derived by simultaneous solution to steady state constraints of power system and the doubly-fed induction generators (DFIGs). A hybrid probabilistic simulation is newly proposed, with the fault branches enumerated and probabilistically weighted, while the fault parameters sampled. The variance coefficient of hybrid Monte-Carlo (HMC) simulation is defined to describe the convergence, which is speeded up by the optimal HMC (OPHMC) with the density function of the fault types. The numerical analysis of IEEE RTS system shows the impacts of high-order fault and wind power by comparing expectation, variance, and distribution of the bus voltage and branch current. The accuracy, convergence, efficiency of Monte-Carlo (MC), HMC and OPHMC methods are compared.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"23 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":"115729490","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.2016.7764083
L. Carvalho, J. Teixeira, M. Matos
The growing integration of renewable energy in power systems demands for adequate planning of generation systems not only to meet long-term capacity requirements but also to cope with sudden capacity shortages that can occur during system operation. As a matter of fact, system operators must schedule an adequate amount of operational reserve to avoid capacity deficits which can be caused by, for instance, overestimating the wind power that will be available. The framework proposed for the long-term assessment of operational reserve relies on the Naïve forecasting method to produce wind power forecasts for the next hour. This forecasting model is simple and widely used to obtain short-term forecasts. However, it has been shown that regression models, such as the Autoregressive Integrated Moving Average (ARIMA) model, can outperform the Naïve model even for forecasting horizons of up to 1 hour. This paper investigates the differences in the risk indices obtained for the long-term operational reserve when using the Naïve and the ARIMA forecasting models. The objective is to assess the impact of the forecasting error in the long-term operational reserve risk indices. Experiments using the Sequential Monte Carlo Simulation (SMCS) method were carried out on a modified version of the IEEE RTS 79 test system that includes wind and hydro power variability. A sensitivity analysis was also performed taking into account several wind power integration scenarios and two different merit orders for scheduling generating units.
{"title":"Modeling wind power uncertainty in the long-term operational reserve adequacy assessment: A comparative analysis between the Naïve and the ARIMA forecasting models","authors":"L. Carvalho, J. Teixeira, M. Matos","doi":"10.1109/PMAPS.2016.7764083","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764083","url":null,"abstract":"The growing integration of renewable energy in power systems demands for adequate planning of generation systems not only to meet long-term capacity requirements but also to cope with sudden capacity shortages that can occur during system operation. As a matter of fact, system operators must schedule an adequate amount of operational reserve to avoid capacity deficits which can be caused by, for instance, overestimating the wind power that will be available. The framework proposed for the long-term assessment of operational reserve relies on the Naïve forecasting method to produce wind power forecasts for the next hour. This forecasting model is simple and widely used to obtain short-term forecasts. However, it has been shown that regression models, such as the Autoregressive Integrated Moving Average (ARIMA) model, can outperform the Naïve model even for forecasting horizons of up to 1 hour. This paper investigates the differences in the risk indices obtained for the long-term operational reserve when using the Naïve and the ARIMA forecasting models. The objective is to assess the impact of the forecasting error in the long-term operational reserve risk indices. Experiments using the Sequential Monte Carlo Simulation (SMCS) method were carried out on a modified version of the IEEE RTS 79 test system that includes wind and hydro power variability. A sensitivity analysis was also performed taking into account several wind power integration scenarios and two different merit orders for scheduling generating units.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"19 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":"115287270","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.2016.7764082
Robert Brandalik, Dominik Waeresch, W. Wellssow
The large feed-ins of photovoltaic (PV) systems in low voltage (LV) grids result in increasing voltage magnitudes and line loadings. While the rise of voltage magnitudes can be limited e.g. by distribution transformers (DTs) with on-load tap changers, high line loadings cannot even be detected by network operators due to a lack of network observability. LV state estimation (SE) systems can provide a way to determine the required network states and line loadings. Measured operational network variables, e.g. voltage magnitudes and power values of PV systems, can be used as input data for the SE. Nevertheless, power measurements of households are not available and thus the household loads have to be approximately determined. This paper presents approximate active power distributions (AAPDs) for standard household loads, derived on the basis of field-trial data. They are an innovative way for the necessary generation of active power pseudo-values (APPVs) for LV SE with statistical errors following a Gaussian distribution. Despite the simplicity of the AAPDs the errors made within the current calculation is acceptable.
{"title":"Approximate active power distributions for standard household loads","authors":"Robert Brandalik, Dominik Waeresch, W. Wellssow","doi":"10.1109/PMAPS.2016.7764082","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764082","url":null,"abstract":"The large feed-ins of photovoltaic (PV) systems in low voltage (LV) grids result in increasing voltage magnitudes and line loadings. While the rise of voltage magnitudes can be limited e.g. by distribution transformers (DTs) with on-load tap changers, high line loadings cannot even be detected by network operators due to a lack of network observability. LV state estimation (SE) systems can provide a way to determine the required network states and line loadings. Measured operational network variables, e.g. voltage magnitudes and power values of PV systems, can be used as input data for the SE. Nevertheless, power measurements of households are not available and thus the household loads have to be approximately determined. This paper presents approximate active power distributions (AAPDs) for standard household loads, derived on the basis of field-trial data. They are an innovative way for the necessary generation of active power pseudo-values (APPVs) for LV SE with statistical errors following a Gaussian distribution. Despite the simplicity of the AAPDs the errors made within the current calculation is acceptable.","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":"115982271","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.2016.7764173
E. Shayesteh, P. Hilber
Asset management is an important topic in all fields especially in power system which has very high investment costs and very expensive elements. Reliability Centered Asset Management (RCAM) is an effective technique to perform the power system asset management with quantitative methods such that, on the one hand, the total cost is minimized and, on the other hand, the reliability of the system is maximized. Nevertheless, the need for an appropriate optimization-based algorithm for RCAM implementation in power system is still sensed. This paper proposes an algorithm to fulfil such needs including the following steps. First, the component reliability importance index is calculated for all components of the system. Then, a set of all potential maintenance strategies of each component are defined and together with the component reliability importance indices are used as inputs in the third step. In the third step, an optimization problem is proposed to select the optimum maintenance strategy for each component in the system. The proposed three-step algorithm is tested on a Swedish distribution system. The results highlight the advantages of the proposed method for well-organizing the maintenance strategies for all components of the system.
{"title":"Reliability-centered asset management using component reliability importance","authors":"E. Shayesteh, P. Hilber","doi":"10.1109/PMAPS.2016.7764173","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764173","url":null,"abstract":"Asset management is an important topic in all fields especially in power system which has very high investment costs and very expensive elements. Reliability Centered Asset Management (RCAM) is an effective technique to perform the power system asset management with quantitative methods such that, on the one hand, the total cost is minimized and, on the other hand, the reliability of the system is maximized. Nevertheless, the need for an appropriate optimization-based algorithm for RCAM implementation in power system is still sensed. This paper proposes an algorithm to fulfil such needs including the following steps. First, the component reliability importance index is calculated for all components of the system. Then, a set of all potential maintenance strategies of each component are defined and together with the component reliability importance indices are used as inputs in the third step. In the third step, an optimization problem is proposed to select the optimum maintenance strategy for each component in the system. The proposed three-step algorithm is tested on a Swedish distribution system. The results highlight the advantages of the proposed method for well-organizing the maintenance strategies for all components of the system.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"124 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":"131157105","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.2016.7764213
Yingmeng Xiang, Lingfeng Wang, Nian Liu, Ruosong Xiao, K. Xie
Power system operation is facing increasing cyber and physical attack risks and it is pressing to develop effective methods to improve the resiliency of electric power infrastructure against malicious attacks. In this study, a holistic resiliency framework is proposed by extending the conventional security-constrained optimal power flow analysis (SCOPF) to incorporate the presumed risk caused by the attacks. The improved solution method is studied by combining particle swarm optimization, primal-dual interior point (PDIP) method and parallel computing. The case studies conducted on IEEE 39-bus and 118-bus systems demonstrate the proposed SCOPF model is able to improve the resiliency of power system for the presumed attacks. This study can provide some meaningful insights on improving the power system operation resiliency.
{"title":"A resilient power system operation strategy considering presumed attacks","authors":"Yingmeng Xiang, Lingfeng Wang, Nian Liu, Ruosong Xiao, K. Xie","doi":"10.1109/PMAPS.2016.7764213","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764213","url":null,"abstract":"Power system operation is facing increasing cyber and physical attack risks and it is pressing to develop effective methods to improve the resiliency of electric power infrastructure against malicious attacks. In this study, a holistic resiliency framework is proposed by extending the conventional security-constrained optimal power flow analysis (SCOPF) to incorporate the presumed risk caused by the attacks. The improved solution method is studied by combining particle swarm optimization, primal-dual interior point (PDIP) method and parallel computing. The case studies conducted on IEEE 39-bus and 118-bus systems demonstrate the proposed SCOPF model is able to improve the resiliency of power system for the presumed attacks. This study can provide some meaningful insights on improving the power system operation resiliency.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"17 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114085404","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.2016.7764140
Alexander Rhein, G. Balzer, Raoul Boya, C. Eichler
Assets in transmission systems are maintained and replaced according to the time-based strategy. This contribution identifies the individual importance of each asset for the availability of the grid with the help of reliability calculations and improves the allocation of maintenance and replacement activities. The optimization is performed by particle swarm algorithm. It determines the intensity of the maintenance and the year of the replacement for each asset of the grid individually. By optimizing capital expenditures, operational expenditures, and the availability of the grid, this method improves the maintenance and replacement strategy with the help of Pareto optimality. At the end of the contribution, the benefits of optimized maintenance and replacement strategies are pointed out exemplarily for a part of a 220 kV grid.
{"title":"Multi-criteria optimization of maintenance and replacement strategies in transmission systems","authors":"Alexander Rhein, G. Balzer, Raoul Boya, C. Eichler","doi":"10.1109/PMAPS.2016.7764140","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764140","url":null,"abstract":"Assets in transmission systems are maintained and replaced according to the time-based strategy. This contribution identifies the individual importance of each asset for the availability of the grid with the help of reliability calculations and improves the allocation of maintenance and replacement activities. The optimization is performed by particle swarm algorithm. It determines the intensity of the maintenance and the year of the replacement for each asset of the grid individually. By optimizing capital expenditures, operational expenditures, and the availability of the grid, this method improves the maintenance and replacement strategy with the help of Pareto optimality. At the end of the contribution, the benefits of optimized maintenance and replacement strategies are pointed out exemplarily for a part of a 220 kV grid.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"141 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":"114759364","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.2016.7763927
C. Singh, Shijia Zhao
Conditions for equivalence of state probabilities obtained from the data on state residence times and those from data on interstate transitions are explored in this paper. The derived conditions are useful in applications under various situations. The situations illustrated in this paper include when data is available only for state residence times but a state transition rate matrix needs to be developed for purposes of application. A situation is also illustrated when data on state residence times and interstate transitions is collected but inaccuracies may exist in the collection or processing of interstate data. Another condition explored is the effect of the probability distribution of state residence times on the reliability indices.
{"title":"Investigation of equivalence between the interstate transition rates and state probabilities in the data analysis and applications","authors":"C. Singh, Shijia Zhao","doi":"10.1109/PMAPS.2016.7763927","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7763927","url":null,"abstract":"Conditions for equivalence of state probabilities obtained from the data on state residence times and those from data on interstate transitions are explored in this paper. The derived conditions are useful in applications under various situations. The situations illustrated in this paper include when data is available only for state residence times but a state transition rate matrix needs to be developed for purposes of application. A situation is also illustrated when data on state residence times and interstate transitions is collected but inaccuracies may exist in the collection or processing of interstate data. Another condition explored is the effect of the probability distribution of state residence times on the reliability indices.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"55 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":"114786675","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.2016.7764225
K. Xie, Shuwei Miao, Yun Xia, Yinghao Ma, Yanlin Li
Collected wind speed time series (WSTS) has three major characteristics: randomness, autocorrelation and cross-correlation, which have significant effects on the wind speed modeling for power systems containing wind energies. Most WSTS models only consider some of the above characteristics, which may significantly reduce the computation accuracy on the analysis of wind-integrated power systems. This paper presents a two-stage model for WSTS at multiple wind sites. This model considers the wind speed autocorrelation for each WSTS in the first stage, and wind speed cross-correlation for all WSTSs in the second stage. The inverse transformation is used to derive the analytical correlation relationship between multiple WSTSs and multiple time series of normal distribution (TSND). Then modeling multiple WSTSs with given correlations can be done by building multiple TSNDs that meet appropriate autocorrelations and cross-correlations using an autoregressive model. Case studies demonstrate that the proposed model is capable of simulating WSTS with higher accuracy than the improved correlation method, the time-shifting technique, and the Copula method.
{"title":"A two-stage wind speed model for multiple wind farms considering autocorrelations and cross-correlations","authors":"K. Xie, Shuwei Miao, Yun Xia, Yinghao Ma, Yanlin Li","doi":"10.1109/PMAPS.2016.7764225","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764225","url":null,"abstract":"Collected wind speed time series (WSTS) has three major characteristics: randomness, autocorrelation and cross-correlation, which have significant effects on the wind speed modeling for power systems containing wind energies. Most WSTS models only consider some of the above characteristics, which may significantly reduce the computation accuracy on the analysis of wind-integrated power systems. This paper presents a two-stage model for WSTS at multiple wind sites. This model considers the wind speed autocorrelation for each WSTS in the first stage, and wind speed cross-correlation for all WSTSs in the second stage. The inverse transformation is used to derive the analytical correlation relationship between multiple WSTSs and multiple time series of normal distribution (TSND). Then modeling multiple WSTSs with given correlations can be done by building multiple TSNDs that meet appropriate autocorrelations and cross-correlations using an autoregressive model. Case studies demonstrate that the proposed model is capable of simulating WSTS with higher accuracy than the improved correlation method, the time-shifting technique, and the Copula method.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"31 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120855681","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.2016.7764208
Farzaneh Pourahmadi, M. Jooshaki, S. H. Hosseini
Minimization of operating costs is one of the most important objectives of power system operators. To achieve this goal, several optimization problems such as unit commitment and optimal power flow have been introduced. Historically, in these problems, the transmission network has been considered as a static system, i.e., the ability of transmission lines switching is not modeled. On the other hand, it has been shown that transmission line switching can significantly reduce operating costs by the means of topology modification. However, considering this capability, a large number of binary variables are introduced in the objective function, and as a consequence, the computation time will be considerably increased. To address this problem, this paper tries to propose an effective method based on the dynamic programming algorithm for solving the optimal transmission switching (OTS). In this method, firstly the OTS is modeled as a step by step problem. Then, in order to reduce the computation time, in each step, some lines are chosen as candidates for outage by using appropriate criteria. The proposed method not only reduces the computation time but also considers the effects of transmission switching on the operational constraints that have not been modeled in the previous DC models. It is also shown that the method can effectively consider the N-1 security criterion. Finally, in order to illustrate the effectiveness of the proposed method, it is applied to the IEEE 118-Bus test system and the results are discussed.
{"title":"A dynamic programming-based heuristic approach for optimal transmission switching problem with N-1 reliability criterion","authors":"Farzaneh Pourahmadi, M. Jooshaki, S. H. Hosseini","doi":"10.1109/PMAPS.2016.7764208","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764208","url":null,"abstract":"Minimization of operating costs is one of the most important objectives of power system operators. To achieve this goal, several optimization problems such as unit commitment and optimal power flow have been introduced. Historically, in these problems, the transmission network has been considered as a static system, i.e., the ability of transmission lines switching is not modeled. On the other hand, it has been shown that transmission line switching can significantly reduce operating costs by the means of topology modification. However, considering this capability, a large number of binary variables are introduced in the objective function, and as a consequence, the computation time will be considerably increased. To address this problem, this paper tries to propose an effective method based on the dynamic programming algorithm for solving the optimal transmission switching (OTS). In this method, firstly the OTS is modeled as a step by step problem. Then, in order to reduce the computation time, in each step, some lines are chosen as candidates for outage by using appropriate criteria. The proposed method not only reduces the computation time but also considers the effects of transmission switching on the operational constraints that have not been modeled in the previous DC models. It is also shown that the method can effectively consider the N-1 security criterion. Finally, in order to illustrate the effectiveness of the proposed method, it is applied to the IEEE 118-Bus test system and the results are discussed.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123922457","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.2016.7764209
N. Nguyen, M. Benidris, J. Mitra
This paper proposes a new method to model wind generation in power system reliability evaluation that not only considers the uncertainty of wind speed and mechanical failures of wind turbines but also includes the impacts of wind's low inertia property. Due to the stochasticity and low inertia of wind generation, power system stability and reliability are significantly affected. When wind generators are integrated into the grid, a strategy to ensure the system stability is that wind generators are required to operate at a lower level than their maximum available output power. The effect of this requirement is that not all of the available wind power will be used in the system, which in turn affects the contribution of wind generation in power system availability. The proposed model is implemented using Monte Carlo methods. For every system state, the maximum integrated amount of wind power is determined based on frequency regulation requirements. Then, this amount of power is used along with the stochastic model of wind speed in the reliability modeling. The proposed method is demonstrated on the IEEE RTS system. Power system reliability with and without considering the impacts of wind stochasticity and low inertia are compared to show the effectiveness of the proposed method.
{"title":"A unified analysis of the impacts of stochasticity and low inertia of wind generation","authors":"N. Nguyen, M. Benidris, J. Mitra","doi":"10.1109/PMAPS.2016.7764209","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764209","url":null,"abstract":"This paper proposes a new method to model wind generation in power system reliability evaluation that not only considers the uncertainty of wind speed and mechanical failures of wind turbines but also includes the impacts of wind's low inertia property. Due to the stochasticity and low inertia of wind generation, power system stability and reliability are significantly affected. When wind generators are integrated into the grid, a strategy to ensure the system stability is that wind generators are required to operate at a lower level than their maximum available output power. The effect of this requirement is that not all of the available wind power will be used in the system, which in turn affects the contribution of wind generation in power system availability. The proposed model is implemented using Monte Carlo methods. For every system state, the maximum integrated amount of wind power is determined based on frequency regulation requirements. Then, this amount of power is used along with the stochastic model of wind speed in the reliability modeling. The proposed method is demonstrated on the IEEE RTS system. Power system reliability with and without considering the impacts of wind stochasticity and low inertia are compared to show the effectiveness of the proposed method.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"14 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":"121504184","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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