2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)最新文献_第8页

Parallel GPU Implementation for Fast Generating System Adequacy Assessment via Sequential Monte Carlo Simulation 通过顺序蒙特卡罗模拟快速生成系统充分性评估的并行GPU实现

2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) Pub Date : 2020-08-01 DOI: 10.1109/PMAPS47429.2020.9183554

Inês M. Alves, Vladimiro Miranda, L. Carvalho

{"title":"Parallel GPU Implementation for Fast Generating System Adequacy Assessment via Sequential Monte Carlo Simulation","authors":"Inês M. Alves, Vladimiro Miranda, L. Carvalho","doi":"10.1109/PMAPS47429.2020.9183554","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183554","url":null,"abstract":"The Sequential Monte Carlo Simulation (SMCS) is a powerful and flexible method commonly used for generating system adequacy assessment. By sampling outage events in sequence and their respective duration, this method can easily incorporate time-dependent issues such as renewable power production, the capacity of hydro units, scheduled maintenance, complex correlated load models, etc, and is the only method that provides probability distributions for the reliability indexes. Despite these advantages, the SMCS method requires considerably more simulation time than the Non-sequential Monte Carlo Simulation approach to provide accurate estimates for the reliability indexes. In an attempt to reduce the simulation time, the SMCS method has been implemented in parallel using a Graphics Processing Unit (GPU) to take advantage of the fast calculations provided by these computing platforms. Two parallelization strategies are proposed: Strategy A, which creates and evaluates yearly samples in a completely parallel approach and while the estimates of the reliability indexes are computed in the CPU; and Strategy B, which consists on concurrently sampling the outage events for the generating units while the state evaluation and the index estimation stages are executed in serial. Simulation results for the IEEE RTS 79, IEEE RTS 96, and the new IEEE RTS GMLC test systems, show that both implementations lead to a significant acceleration of the SMCS method while keeping all its advantages. In addition, it was observed that Strategy B results in less simulation time than Strategy A for generation system adequacy assessment.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"220 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114278141","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}

引用次数: 0

First Order Non-homogeneous Markov Chain Model for Generation of Wind Speed and Direction Synthetic Time Series 风速与风向合成时间序列生成的一阶非齐次马尔可夫链模型

2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) Pub Date : 2020-08-01 DOI: 10.1109/PMAPS47429.2020.9183446

V. Di Giorgio, R. Langella, A. Testa, S. Djokic, M. Zou

引用次数: 4

Performance of probabilistic disturbance forecasts in extreme weather on the Icelandic power system 冰岛电力系统极端天气下概率扰动预报的性能

2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) Pub Date : 2020-08-01 DOI: 10.1109/PMAPS47429.2020.9183655

S. Perkin, Arnbjörg Arnardóttir, K. Sigurjonsson, Þorvaldur Jacobsen

引用次数: 1

PMAPS 2020 Cover Page PMAPS 2020封面

2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) Pub Date : 2020-08-01 DOI: 10.1109/pmaps47429.2020.9183677

引用次数: 0

ProbCast: Open-source Production, Evaluation and Visualisation of Probabilistic Forecasts ProbCast:概率预测的开源生产、评估和可视化

2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) Pub Date : 2020-08-01 DOI: 10.1109/PMAPS47429.2020.9183441

J. Browell, C. Gilbert

引用次数: 7

General Polynomial Chaos vs Crude Monte Carlo for Probabilistic Evaluation of Distribution Systems 分配系统概率评估的一般多项式混沌与粗糙蒙特卡罗

2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) Pub Date : 2020-08-01 DOI: 10.1109/PMAPS47429.2020.9183453

Arpan Koirala, T. Acker, D. Van Hertem, Juliano Camargo, R. D’hulst

引用次数: 5

Parameter Estimation for Distribution Grid Reliability Assessment 配电网可靠性评估的参数估计

2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) Pub Date : 2020-08-01 DOI: 10.1109/PMAPS47429.2020.9183408

Raphael Wu, G. Sansavini

引用次数: 2

Fast Probabilistic Optimal Power Flow Based on Modified Multi-Parametric Programming 基于修正多参数规划的快速概率最优潮流

2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) Pub Date : 2020-08-01 DOI: 10.1109/PMAPS47429.2020.9183581

Wei Lin, Juan Yu, Zhifang Yang, Xuebin Wang

引用次数: 1

Atmospheric circulation archetypes as clustering criteria for wind power inputs into probabilistic power flow analysis 大气环流原型作为风电输入概率潮流分析的聚类准则

2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) Pub Date : 2020-08-01 DOI: 10.1109/PMAPS47429.2020.9183659

A. Dalton, B. Bekker, M. Koivisto

{"title":"Atmospheric circulation archetypes as clustering criteria for wind power inputs into probabilistic power flow analysis","authors":"A. Dalton, B. Bekker, M. Koivisto","doi":"10.1109/PMAPS47429.2020.9183659","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183659","url":null,"abstract":"The variability of the wind resource is the primary challenge associated with the introduction of large scale wind power onto electricity networks. In addressing uncertainties associated with wind power, probabilistic power flow analysis (PPF) is often used in resolving current or future system states. These simulations are informed by input scenarios - i.e. time series that represent conditions being simulated. Thereby defining appropriate input scenarios is a critically important part of the process. This study proposes a novel methodology for clustering wind power time series based on the dominant concurrent atmospheric circulation patterns. These patterns are classified into generalized architypes using Self Organizing Maps. Thereby the probabilistic properties and data dependency structures of wind farms are approximated at the hand of the causative weather phenomena. It was found that this methodology resulted in significant variations in the probabilistic properties of wind power time series and the correlations between wind generators. It is anticipated that this methodology could be effectively applied in defining the input characteristics into operational scenarios within a PPF analysis, and inform correlations between wind farms in spatiotemporal probabilistic forecasts.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125716947","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}

引用次数: 3

Predictive and Cooperative Voltage Control with Probabilistic Load and Solar Generation Forecasting 基于概率负荷与太阳能发电预测的电压预测与协同控制

2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) Pub Date : 2020-08-01 DOI: 10.1109/PMAPS47429.2020.9183699

Shahrzad Mahdavi, Hossein Panamtash, A. Dimitrovski, Qun Zhou

{"title":"Predictive and Cooperative Voltage Control with Probabilistic Load and Solar Generation Forecasting","authors":"Shahrzad Mahdavi, Hossein Panamtash, A. Dimitrovski, Qun Zhou","doi":"10.1109/PMAPS47429.2020.9183699","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183699","url":null,"abstract":"This paper proposes predictive cooperative voltage control method in a power system with high penetration of photovoltaic (PV) units. Cooperative distributed control of the reactive power output of PV inverters is coordinated with operation of voltage regulators (VRs) to maintain system voltages within an appropriate bandwidth. Probabilistic forecasting of the solar power generation and the loads is applied to estimate voltage changes which, in turn, are used to set the VR tap positions for preventing large voltage fluctuations with the lowest risk considering the voltage distribution estimation. The fine tuning of voltage adjustment is achieved by cooperative control of PV inverters to maintain a uniform voltage profile across the system. The proposed method is tested on the modified IEEE 123-node test feeder with high PV penetration using real insolation data and with constant loads replaced by several different load profiles. Simulation results demonstrate the effectiveness of the coordinated approach for voltage control with cooperative PV and predictive VR controls taking into account probabilistic load and solar power forecasts.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131593364","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}

引用次数: 9