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Title:Renewable electric power systems energy yield and performance estimation
Author(s):Dhople, Sairaj V.
Director of Research:Domínguez-García, Alejandro D.
Doctoral Committee Chair(s):Domínguez-García, Alejandro D.
Doctoral Committee Member(s):Krein, Philip T.; Sauer, Peter W.; DeVille, Lee; Pilawa-Podgurski, Robert C.
Department / Program:Electrical & Computer Eng
Discipline:Electrical & Computer Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Markov models
Stochastic hybrid systems
Photovoltaic energy conversion systems
Wind energy conversion systems
Abstract:This dissertation presents models for reliability assessment, energy yield estimation, and uncertainty analysis of renewable electric power systems. We propose system performability models that describe system attributes while acknowledging failures and repairs in constituent elements. Two broad classes of models are investigated: i) Markov reliability and reward models, and ii) Stochastic hybrid systems (SHS) models. Conventional Markov models capture attributes that are largely static-the only dynamics are due to changes in system configuration due to failures and repairs in constituent elements. On the other hand, SHS can model a wide variety of dynamic phenomena, and provide significant flexibility over Markov models. From an applications perspective, we propose Markov reward models to estimate the performability of photovoltaic energy conversion systems (PVECS) and wind energy conversion systems (WECS). A major impediment in formulating these models is the lack of precise data on model parameters, e.g., component failure and repair rates. Additionally, inputs to these models (e.g., incident insolation in PVECS and wind speed in WECS) are inherently uncertain. Therefore, to ensure validity of the results, we propose set-theoretic and probabilistic methods for uncertainty analysis in these models. With regard to SHS, we first demonstrate how Markov reliability/reward models are a type of SHS. We also present applications to stochastic small-signal modeling of power systems. Case studies demonstrate how to quantify the impact of renewable resources uncertainty on power system dynamics.
Issue Date:2013-02-03
Rights Information:Copyright 2012 Sairaj Dhople
Date Available in IDEALS:2013-02-03
Date Deposited:2012-12

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