Pub Date : 2020-08-01DOI: 10.1109/PMAPS47429.2020.9183615
R. Çağlar, Tayfun Aydin
The main objective of this paper is to present a reliability evaluation method for the electric power substation of the Light Rail DC Traction Electrification System. The Light Rail Systems which is a special part of rail systems will be introduced, power traction of Light Rail Systems (LRS) will be explained and the reliability analysis of power traction in LRS will be performed. In this study, the structural reliability model of the power substation is developed on physical-based modeling of the components and system configuration. This study only concentrates on determining the reliability of a substation, not including system-wide effects. The reliability analysis is carried out individually for each unit that is composing the system, and then the total reliability of the system is determined according to various scenarios. The reliability analysis of power supply to the traction is performed for a real system named Habipler- Topkapi LRS which is located in İstanbul, Turkey.
{"title":"Electric Power Substation Reliability Assessment for Light Railway DC Traction System","authors":"R. Çağlar, Tayfun Aydin","doi":"10.1109/PMAPS47429.2020.9183615","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183615","url":null,"abstract":"The main objective of this paper is to present a reliability evaluation method for the electric power substation of the Light Rail DC Traction Electrification System. The Light Rail Systems which is a special part of rail systems will be introduced, power traction of Light Rail Systems (LRS) will be explained and the reliability analysis of power traction in LRS will be performed. In this study, the structural reliability model of the power substation is developed on physical-based modeling of the components and system configuration. This study only concentrates on determining the reliability of a substation, not including system-wide effects. The reliability analysis is carried out individually for each unit that is composing the system, and then the total reliability of the system is determined according to various scenarios. The reliability analysis of power supply to the traction is performed for a real system named Habipler- Topkapi LRS which is located in İstanbul, Turkey.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122278309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-22DOI: 10.1109/PMAPS47429.2020.9183597
M. Heyns, S. Lotz, C. Gaunt
As reliance on power networks has increased over the last century, the risk of damage from geomagnetically induced currents (GICs) has become a concern to utilities. The current state of the art in GIC modelling requires significant geophysical modelling and a theoretically derived network response, but has limited empirical validation. In this work, we introduce a probabilistic engineering step between the measured geomagnetic field and GICs, without needing data about the power system topology or the ground conductivity profiles. The resulting empirical ensembles are used to analyse the TVA network (southeastern USA) in terms of peak and cumulative exposure to 5 moderate to intense geomagnetic storms. Multiple nodes are ranked according to susceptibility and the measured response of the total TVA network is further calibrated to existing extreme value models. The probabilistic engineering step presented can complement present approaches, being particularly useful for risk assessment of existing transformers and power systems.
{"title":"Probabilistic Analysis of Power Network Susceptibility to GICs","authors":"M. Heyns, S. Lotz, C. Gaunt","doi":"10.1109/PMAPS47429.2020.9183597","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183597","url":null,"abstract":"As reliance on power networks has increased over the last century, the risk of damage from geomagnetically induced currents (GICs) has become a concern to utilities. The current state of the art in GIC modelling requires significant geophysical modelling and a theoretically derived network response, but has limited empirical validation. In this work, we introduce a probabilistic engineering step between the measured geomagnetic field and GICs, without needing data about the power system topology or the ground conductivity profiles. The resulting empirical ensembles are used to analyse the TVA network (southeastern USA) in terms of peak and cumulative exposure to 5 moderate to intense geomagnetic storms. Multiple nodes are ranked according to susceptibility and the measured response of the total TVA network is further calibrated to existing extreme value models. The probabilistic engineering step presented can complement present approaches, being particularly useful for risk assessment of existing transformers and power systems.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131297496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-27DOI: 10.1109/PMAPS47429.2020.9183552
Ashley M. Hou, Line A. Roald
In this paper, we consider a chance-constrained formulation of the optimal power flow problem to handle uncertainties resulting from renewable generation and load variability. We propose a tuning method that iterates between solving an approximated reformulation of the optimization problem and using a posteriori sample-based evaluations to refine the reformulation. Our method is applicable to both single and joint chance constraints and does not rely on any distributional assumptions on the uncertainty. In a case study for the IEEE 24-bus system, we demonstrate that our method is computationally efficient and enforces chance constraints without over-conservatism.
{"title":"Chance Constraint Tuning for Optimal Power Flow","authors":"Ashley M. Hou, Line A. Roald","doi":"10.1109/PMAPS47429.2020.9183552","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183552","url":null,"abstract":"In this paper, we consider a chance-constrained formulation of the optimal power flow problem to handle uncertainties resulting from renewable generation and load variability. We propose a tuning method that iterates between solving an approximated reformulation of the optimization problem and using a posteriori sample-based evaluations to refine the reformulation. Our method is applicable to both single and joint chance constraints and does not rely on any distributional assumptions on the uncertainty. In a case study for the IEEE 24-bus system, we demonstrate that our method is computationally efficient and enforces chance constraints without over-conservatism.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132759928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-27DOI: 10.1109/PMAPS47429.2020.9183687
Tim Janke, Florian Steinke
The reliable estimation of forecast uncertainties is crucial for risk-sensitive optimal decision making. In this paper, we propose implicit generative ensemble post-processing, a novel framework for multivariate probabilistic electricity price forecasting. We use a likelihood-free implicit generative model based on an ensemble of point forecasting models to generate multivariate electricity price scenarios with a coherent dependency structure as a representation of the joint predictive distribution. Our ensemble post-processing method outperforms well-established model combination benchmarks. This is demonstrated on a data set from the German day-ahead market. As our method works on top of an ensemble of domain-specific expert models, it can readily be deployed to other forecasting tasks.
{"title":"Probabilistic multivariate electricity price forecasting using implicit generative ensemble post-processing","authors":"Tim Janke, Florian Steinke","doi":"10.1109/PMAPS47429.2020.9183687","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183687","url":null,"abstract":"The reliable estimation of forecast uncertainties is crucial for risk-sensitive optimal decision making. In this paper, we propose implicit generative ensemble post-processing, a novel framework for multivariate probabilistic electricity price forecasting. We use a likelihood-free implicit generative model based on an ensemble of point forecasting models to generate multivariate electricity price scenarios with a coherent dependency structure as a representation of the joint predictive distribution. Our ensemble post-processing method outperforms well-established model combination benchmarks. This is demonstrated on a data set from the German day-ahead market. As our method works on top of an ensemble of domain-specific expert models, it can readily be deployed to other forecasting tasks.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131946041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-24DOI: 10.1109/PMAPS47429.2020.9183423
V. Dvorkin, J. Kazempour, P. Pinson
We develop a stochastic equilibrium model for an electricity market with asymmetric renewable energy forecasts. In our setting, market participants optimize their profits using public information about a conditional expectation of energy production but use private information about the forecast error distribution. This information is given in the form of samples and incorporated into profit-maximizing optimizations of market participants through chance constraints. We model information asymmetry by varying the sample size of participants’ private information. We show that with more information available, the equilibrium gradually converges to the ideal solution provided by the perfect information scenario. Under information scarcity, however, we show that the market converges to the ideal equilibrium if participants are to infer the forecast error distribution from the statistical properties of the data at hand or share their private forecasts.
{"title":"Chance-Constrained Equilibrium in Electricity Markets With Asymmetric Forecasts","authors":"V. Dvorkin, J. Kazempour, P. Pinson","doi":"10.1109/PMAPS47429.2020.9183423","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183423","url":null,"abstract":"We develop a stochastic equilibrium model for an electricity market with asymmetric renewable energy forecasts. In our setting, market participants optimize their profits using public information about a conditional expectation of energy production but use private information about the forecast error distribution. This information is given in the form of samples and incorporated into profit-maximizing optimizations of market participants through chance constraints. We model information asymmetry by varying the sample size of participants’ private information. We show that with more information available, the equilibrium gradually converges to the ideal solution provided by the perfect information scenario. Under information scarcity, however, we show that the market converges to the ideal equilibrium if participants are to infer the forecast error distribution from the statistical properties of the data at hand or share their private forecasts.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125043823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-12DOI: 10.1109/PMAPS47429.2020.9183660
R. Mochamad, A. Ehsan, R. Preece
This paper presents the application of a probabilistic multi-stability assessment of a modified two-area system under the presence of low and high uncertainty sources. The stability of the network is assessed under four stability regimes: frequency, small-signal rotor angle, large-signal rotor angle, and long-term voltage. The probabilistic assessment is carried out using Monte Carlo simulation. Two cases considering low and high uncertainty are investigated. The obtained results are presented in the form of parallel coordinate plots so that the interaction between multiple stability regimes can be more easily understood. It is observed that the poor response of small-signal rotor angle stability generally corresponds to poor response of other stability types in low uncertainty case. However, once the level of uncertainty increases and more sources of uncertainty exist, this relationship is significantly changed.
{"title":"Probabilistic Multi-Stability Assessment in Power Systems with Uncertain Wind Generation","authors":"R. Mochamad, A. Ehsan, R. Preece","doi":"10.1109/PMAPS47429.2020.9183660","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183660","url":null,"abstract":"This paper presents the application of a probabilistic multi-stability assessment of a modified two-area system under the presence of low and high uncertainty sources. The stability of the network is assessed under four stability regimes: frequency, small-signal rotor angle, large-signal rotor angle, and long-term voltage. The probabilistic assessment is carried out using Monte Carlo simulation. Two cases considering low and high uncertainty are investigated. The obtained results are presented in the form of parallel coordinate plots so that the interaction between multiple stability regimes can be more easily understood. It is observed that the poor response of small-signal rotor angle stability generally corresponds to poor response of other stability types in low uncertainty case. However, once the level of uncertainty increases and more sources of uncertainty exist, this relationship is significantly changed.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126885811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-11DOI: 10.1109/PMAPS47429.2020.9183604
A. Ehsan, R. Preece, Seyed Hamid Reza Hosseini, A. Allahham, P. Taylor
This work presents a sequential Monte Carlo-based integrated gas and power flow (IGPF) model to quantify how different sources of uncertainty propagate within the integrated gas and electricity network (IGEN). The uncertain input parameters, i.e. photovoltaic and wind generation, and electricity and heat demand are represented by weekly probabilistic time-series profiles. The time-series profiles of photovoltaic and wind generation are determined using respective Markov chains, whereas the fluctuations in time-series profiles of electricity and heat demand are modelled to comply with respective Gaussian distributions. The goodness-of-fit of these probabilistic time-series profiles to respective historical datasets is evaluated using the Kolmogorov-Smirnov test. Subsequently, the operation of gas and electricity networks, coupled through power-to-gas technology, is simulated using the sequential Monte Carlo-based IGPF model. The effectiveness of proposed approach is assessed through a case study in a localised energy network. Finally, four test-cases are designed to investigate the impact of increasing renewable penetration levels on uncertainty propagation in IGEN.
{"title":"Uncertainty Propagation through Integrated Gas and Electricity Networks using Sequential Monte-Carlo","authors":"A. Ehsan, R. Preece, Seyed Hamid Reza Hosseini, A. Allahham, P. Taylor","doi":"10.1109/PMAPS47429.2020.9183604","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183604","url":null,"abstract":"This work presents a sequential Monte Carlo-based integrated gas and power flow (IGPF) model to quantify how different sources of uncertainty propagate within the integrated gas and electricity network (IGEN). The uncertain input parameters, i.e. photovoltaic and wind generation, and electricity and heat demand are represented by weekly probabilistic time-series profiles. The time-series profiles of photovoltaic and wind generation are determined using respective Markov chains, whereas the fluctuations in time-series profiles of electricity and heat demand are modelled to comply with respective Gaussian distributions. The goodness-of-fit of these probabilistic time-series profiles to respective historical datasets is evaluated using the Kolmogorov-Smirnov test. Subsequently, the operation of gas and electricity networks, coupled through power-to-gas technology, is simulated using the sequential Monte Carlo-based IGPF model. The effectiveness of proposed approach is assessed through a case study in a localised energy network. Finally, four test-cases are designed to investigate the impact of increasing renewable penetration levels on uncertainty propagation in IGEN.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114241045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-12DOI: 10.1109/PMAPS47429.2020.9183690
M. Anjos, J. Cruise, A. Vilalta
Energy storage and demand-side response will play an increasingly important role in the future electricity system. We extend previous results on a single energy storage unit to the management of two energy storage units cooperating for the purpose of price arbitrage. We consider a deterministic dynamic programming model for the cooperative problem, which accounts for market impact. We develop the Lagrangian theory and present a new algorithm to identify pairs of strategies. While we are not able to prove that the algorithm provides optimal strategies, we give strong numerical evidence in favour of it. Furthermore, the Lagrangian approach makes it possible to identify decision and forecast horizons, the latter being a time beyond which it is not necessary to look in order to determine the present optimal action. In practice, this allows for real-time reoptimization, with both horizons being of the order of days.
{"title":"Control of Two Energy Storage Units with Market Impact: Lagrangian Approach and Horizons","authors":"M. Anjos, J. Cruise, A. Vilalta","doi":"10.1109/PMAPS47429.2020.9183690","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183690","url":null,"abstract":"Energy storage and demand-side response will play an increasingly important role in the future electricity system. We extend previous results on a single energy storage unit to the management of two energy storage units cooperating for the purpose of price arbitrage. We consider a deterministic dynamic programming model for the cooperative problem, which accounts for market impact. We develop the Lagrangian theory and present a new algorithm to identify pairs of strategies. While we are not able to prove that the algorithm provides optimal strategies, we give strong numerical evidence in favour of it. Furthermore, the Lagrangian approach makes it possible to identify decision and forecast horizons, the latter being a time beyond which it is not necessary to look in order to determine the present optimal action. In practice, this allows for real-time reoptimization, with both horizons being of the order of days.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128622471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-04DOI: 10.1109/PMAPS47429.2020.9183526
Chenguang Wang, Simon Tindemans, Kaikai Pan, P. Palensky
State estimation is of considerable significance for the power system operation and control. However, well-designed false data injection attacks can utilize blind spots in conventional residual-based bad data detection methods to manipulate measurements in a coordinated manner and thus affect the secure operation and economic dispatch of grids. In this paper, we propose a detection approach based on an autoencoder neural network. By training the network on the dependencies intrinsic in ‘normal’ operation data, it effectively overcomes the challenge of unbalanced training data that is inherent in power system attack detection. To evaluate the detection performance of the proposed mechanism, we conduct a series of experiments on the IEEE 118-bus power system. The experiments demonstrate that the proposed autoencoder detector displays robust detection performance under a variety of attack scenarios.
{"title":"Detection of False Data Injection Attacks Using the Autoencoder Approach","authors":"Chenguang Wang, Simon Tindemans, Kaikai Pan, P. Palensky","doi":"10.1109/PMAPS47429.2020.9183526","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183526","url":null,"abstract":"State estimation is of considerable significance for the power system operation and control. However, well-designed false data injection attacks can utilize blind spots in conventional residual-based bad data detection methods to manipulate measurements in a coordinated manner and thus affect the secure operation and economic dispatch of grids. In this paper, we propose a detection approach based on an autoencoder neural network. By training the network on the dependencies intrinsic in ‘normal’ operation data, it effectively overcomes the challenge of unbalanced training data that is inherent in power system attack detection. To evaluate the detection performance of the proposed mechanism, we conduct a series of experiments on the IEEE 118-bus power system. The experiments demonstrate that the proposed autoencoder detector displays robust detection performance under a variety of attack scenarios.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123768559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-02-28DOI: 10.1109/PMAPS47429.2020.9183567
Brendan Patch, B. Zwart
We develop a non-parametric procedure for ranking transmission lines in a power system according to the probability that they will overload due to stochastic renewable generation or demand-side load fluctuations, and compare this procedure to several benchmark approaches. Using the IEEE 39-bus test network we provide evidence that our approach, which statistically estimates the rate function for each line, is highly promising relative to alternative methods which count overload events or use incorrect parametric assumptions.
{"title":"Ranking transmission lines by overload probability using the empirical rate function","authors":"Brendan Patch, B. Zwart","doi":"10.1109/PMAPS47429.2020.9183567","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183567","url":null,"abstract":"We develop a non-parametric procedure for ranking transmission lines in a power system according to the probability that they will overload due to stochastic renewable generation or demand-side load fluctuations, and compare this procedure to several benchmark approaches. Using the IEEE 39-bus test network we provide evidence that our approach, which statistically estimates the rate function for each line, is highly promising relative to alternative methods which count overload events or use incorrect parametric assumptions.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130156025","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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