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1.
-3807-0843.
Transmission Expansion Planning : computational challenges toward real-size networks.
Degree: PhD, Electrical and Computer Engineering, 2017, University of Texas – Austin
URL: http://hdl.handle.net/2152/62068 
► The importance of the transmission network for supplying electricity demand is undeniable, and Transmission Expansion Planning (TEP) studies is key for a reliable power system.…
(more)
▼ The importance of the transmission network for supplying electricity demand is undeniable, and Transmission Expansion Planning (TEP) studies is key for a reliable power system. Due to increasing sources of uncertainty such as more intermittent energy resources, mobile and controllable demands, and fast technology improvements for PVs and energy storage devices, the need for using systematic ways for solving this complex problem is increased. One of the main barriers for deploying optimization-based TEP studies is computationally intractability, which is the main motivation for this research.
The aim of this work is to investigate the computational challenges associated with systematic TEP studies for large-scale problems, and develop algorithms to improve computational performance. In the first step, we investigate the impact of adding security constraints (as NERC standard requirement) into TEP optimization problem, and develop the Variable Contingency List (VCL) algorithm to pre-screen security constraints to only add those that may affect the feasible region. It significantly decreases the size of the problem compared to considering all security constraints. Then, we evaluate the impact of the size of candidate lines list (number of binary variables) on TEP, and developed a heuristic algorithm to decrease the size of this list.
In the next step, we integrate uncertainties into the TEP optimization problem and formulate the problem as a two-stage stochastic program. Adding uncertainties increases the size of the problem significantly. It leads us to develop a three-level filter that introduces important scenario identification index (ISII) and similar scenario elimination (SSE) technique to decrease the number of security constraints in stochastic TEP in a systematic and tractable way.
We then investigate the scalability of the
stochastic TEP formulation. We develop a configurable decomposition framework that allows us to decompose the original problem into subproblems that can be solved independently and in parallel. This framework can benefit from using both progressive hedging (PH) and Benders decomposition (BD) algorithms to decompose and parallelize a large-scale problem both vertically and horizontally. We have also developed a bundling algorithm that improves the performance of PH algorithm and the overall performance of the framework.
We have implemented our work on a reduced ERCOT network with more than 3000 buses to demonstrate the practicality of the proposed method in this work for large-scale problems.
Advisors/Committee Members: Baldick, Ross (advisor), Santoso, Surya (committee member), Arapostathis, Ari (committee member), Bickel, James Eric (committee member), Caramanis, Constantine (committee member).
Subjects/Keywords: Transmission Expansion Planning; Stochastic optimization; Mixed integer programming; Benders decomposition; Progressive hedging
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APA (6th Edition):
-3807-0843. (2017). Transmission Expansion Planning : computational challenges toward real-size networks. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/62068
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Chicago Manual of Style (16th Edition):
-3807-0843. “Transmission Expansion Planning : computational challenges toward real-size networks.” 2017. Doctoral Dissertation, University of Texas – Austin. Accessed February 28, 2021.
http://hdl.handle.net/2152/62068.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
MLA Handbook (7th Edition):
-3807-0843. “Transmission Expansion Planning : computational challenges toward real-size networks.” 2017. Web. 28 Feb 2021.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Vancouver:
-3807-0843. Transmission Expansion Planning : computational challenges toward real-size networks. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2017. [cited 2021 Feb 28].
Available from: http://hdl.handle.net/2152/62068.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Council of Science Editors:
-3807-0843. Transmission Expansion Planning : computational challenges toward real-size networks. [Doctoral Dissertation]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/62068
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
University of Texas – Austin
2.
Sisbot, Emre Arda.
Fluid and queueing networks with Gurvich-type routing.
Degree: PhD, Operations research and industrial engineering, 2015, University of Texas – Austin
URL: http://hdl.handle.net/2152/32536
► Queueing networks have applications in a wide range of domains, from call center management to telecommunication networks. Motivated by a healthcare application, in this dissertation,…
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▼ Queueing networks have applications in a wide range of domains, from call center management to telecommunication networks. Motivated by a healthcare application, in this dissertation, we analyze a class of queueing and fluid networks with an additional routing option that we call Gurvich-type routing. The networks we consider include parallel buffers, each associated with a different class of entity, and Gurvich-type routing allows to control the assignment of an incoming entity to one of the classes. In addition to routing, scheduling of entities is also controlled as the classes of entities compete for service at the same station. A major theme in this work is the investigation of the interplay of this routing option with the scheduling decisions in networks with various topologies. The first part of this work focuses on a queueing network composed of two parallel buffers. We form a Markov decision process representation of this system and prove structural results on the optimal routing and scheduling controls. Via these results, we determine a near-optimal discrete policy by solving the associated fluid model along with perturbation expansions. In the second part, we analyze a single-station fluid network composed of N parallel buffers with an arbitrary N. For this network, along with structural proofs on the optimal scheduling policies, we show that the optimal routing policies are threshold-based. We then develop a numerical procedure to compute the optimal policy for any initial state. The final part of this work extends the analysis of the previous part to tandem fluid networks composed of two stations. For two different models, we provide results on the optimal scheduling and routing policies.
Advisors/Committee Members: Hasenbein, John J. (advisor), Bickel, James Eric (committee member), Cudina, Milica (committee member), Djurdjanovic, Dragan (committee member), Khajavirad, Aida (committee member).
Subjects/Keywords: Markov decision processes; Queueing theory; Optimal control; Fluid model; Scheduling; Routing
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APA (6th Edition):
Sisbot, E. A. (2015). Fluid and queueing networks with Gurvich-type routing. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/32536
Chicago Manual of Style (16th Edition):
Sisbot, Emre Arda. “Fluid and queueing networks with Gurvich-type routing.” 2015. Doctoral Dissertation, University of Texas – Austin. Accessed February 28, 2021.
http://hdl.handle.net/2152/32536.
MLA Handbook (7th Edition):
Sisbot, Emre Arda. “Fluid and queueing networks with Gurvich-type routing.” 2015. Web. 28 Feb 2021.
Vancouver:
Sisbot EA. Fluid and queueing networks with Gurvich-type routing. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2015. [cited 2021 Feb 28].
Available from: http://hdl.handle.net/2152/32536.
Council of Science Editors:
Sisbot EA. Fluid and queueing networks with Gurvich-type routing. [Doctoral Dissertation]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/32536
University of Texas – Austin
3.
-6024-5216.
Uncertainty quantification and its properties for hidden Markov models with application to condition based maintenance.
Degree: PhD, Operations Research and Industrial Engineering, 2018, University of Texas – Austin
URL: http://hdl.handle.net/2152/63715
► Condition-based maintenance (CBM) can be viewed as a transformation of data gathered from a piece of equipment into information about its condition, and further into…
(more)
▼ Condition-based maintenance (CBM) can be viewed as a transformation of data gathered from a piece of equipment into information about its condition, and further into decisions on what to do with the equipment. Hidden Markov model (HMM) is a useful framework to probabilistically model the condition of complex engineering systems with partial observability of the underlying states. Condition monitoring and prediction of such type of system requires accurate knowledge of HMM that describes the degradation of such a system with data collected from the sensors mounted on it, as well as understanding of the uncertainty of the HMMs identified from the available data. To that end, this thesis proposes a novel HMM estimation scheme based on the principles of Bayes theorem. The newly proposed Bayesian estimation approach for estimating HMM parameters naturally yields information about model parametric uncertainties via posterior distributions of HMM parameters emanating from the estimation process. In addition, a novel condition monitoring scheme based on uncertain
HMMs of the degradation process is proposed and demonstrated on a large dataset obtained from a semiconductor manufacturing facility. Portion of the data was used to build operating mode specific HMMs of machine degradation via the newly proposed Bayesian estimation process, while the remainder of the data was used for monitoring of machine condition using the uncertain degradation HMMs yielded by Bayesian estimation. Comparison with a traditional signature-based statistical monitoring method showed that the newly proposed approach effectively utilizes the fact that its parameters are uncertain themselves, leading to orders of magnitude fewer false alarms. This methodology is further extended to address the practical issue that maintenance interventions are usually imperfect. We propose both a novel non-ergodic and non-homogeneous HMM that assumes imperfect maintenances and a novel process monitoring method capable of monitoring the hidden states considering model uncertainty. Significant improvement in both the log-likelihood of estimated HMM parameters and monitoring performance were observed, compared to those obtained using degradation HMMs that always assumed perfect maintenance.
Finally, behavior of the posterior distribution of parameters of unidirectional non- ergodic HMMs modeling in this thesis for degradation was theoretically analyzed in terms of their evolution as more data become available in the estimation process. The convergence problem is formulated as a Bernstein-von Mises theorem (BvMT), and under certain regularity conditions, the sequence of posterior distributions is proven to converge to a Gaussian distribution with variance matrix being the inverse of the Fisher information matrix. An example of a unidirectional HMM is presented for which the regularity conditions are verified, and illustrations of expected theoretical results are given using simulation. The understanding of such convergence of posterior distributions
enables one to…
Advisors/Committee Members: Djurdjanovic, Dragan (advisor), Hasenbein, John (committee member), Bickel, James Eric (committee member), Walker, Stephen G (committee member), Hanasusanto, Grani (committee member).
Subjects/Keywords: Hidden Markov model; Condition-based maintenance
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APA ·
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MLA ·
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APA (6th Edition):
-6024-5216. (2018). Uncertainty quantification and its properties for hidden Markov models with application to condition based maintenance. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/63715
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Chicago Manual of Style (16th Edition):
-6024-5216. “Uncertainty quantification and its properties for hidden Markov models with application to condition based maintenance.” 2018. Doctoral Dissertation, University of Texas – Austin. Accessed February 28, 2021.
http://hdl.handle.net/2152/63715.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
MLA Handbook (7th Edition):
-6024-5216. “Uncertainty quantification and its properties for hidden Markov models with application to condition based maintenance.” 2018. Web. 28 Feb 2021.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Vancouver:
-6024-5216. Uncertainty quantification and its properties for hidden Markov models with application to condition based maintenance. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2018. [cited 2021 Feb 28].
Available from: http://hdl.handle.net/2152/63715.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Council of Science Editors:
-6024-5216. Uncertainty quantification and its properties for hidden Markov models with application to condition based maintenance. [Doctoral Dissertation]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/63715
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
4.
Liu, Chengcheng.
Stability and pricing in Naor's model with arrival rate uncertainty.
Degree: PhD, Operations Research and Industrial Engineering, 2019, University of Texas – Austin
URL: http://dx.doi.org/10.26153/tsw/2856
► Naor's observable queueing model describes an M/M/1 queue with strategic customers and a system manager who maximizes the long-run average revenue rate or social benefit…
(more)
▼ Naor's observable queueing model describes an M/M/1 queue with strategic customers and a system manager who maximizes the long-run average revenue rate or social benefit rate. Customers have identical service values and waiting time costs, assuming the waiting cost is linear in time. A new customer chooses to either enter the system or balk after observing the queue length. The system manager decides on the admission fee, which is assumed to be a constant. The results of Naor's model are: the optimal policy for customers is a threshold policy, and customers enter if and only if the queue length is no larger than a threshold; the revenue-maximizing threshold is no larger than the socially optimal threshold, or equivalently, a revenue maximizer (RM) charges a fee no less than a social optimizer (SO).
This research studies an observable queueing system in which the arrival rate is not known with certainty by either customers or the system manager. The customer population is modeled to be either homogeneous or heterogeneous. We present three different models: static pricing with uncertain arrival rate and heterogeneous customers; state dependent pricing with uncertain arrival rate and homogeneous customers; and state dependent pricing with uncertain arrival rate and heterogeneous customers. We study the system stability, the optimal behavior of customers and the optimal pricing policies of the system manager.
Advisors/Committee Members: Hasenbein, John J. (advisor), Bickel, James Eric (committee member), Hanasusanto, Grani A. (committee member), Cudina, Milica (committee member).
Subjects/Keywords: Naor’s model; Parameter uncertainty; Revenue optimization; Heterogeneous customers
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Record Details
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Liu, C. (2019). Stability and pricing in Naor's model with arrival rate uncertainty. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://dx.doi.org/10.26153/tsw/2856
Chicago Manual of Style (16th Edition):
Liu, Chengcheng. “Stability and pricing in Naor's model with arrival rate uncertainty.” 2019. Doctoral Dissertation, University of Texas – Austin. Accessed February 28, 2021.
http://dx.doi.org/10.26153/tsw/2856.
MLA Handbook (7th Edition):
Liu, Chengcheng. “Stability and pricing in Naor's model with arrival rate uncertainty.” 2019. Web. 28 Feb 2021.
Vancouver:
Liu C. Stability and pricing in Naor's model with arrival rate uncertainty. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2019. [cited 2021 Feb 28].
Available from: http://dx.doi.org/10.26153/tsw/2856.
Council of Science Editors:
Liu C. Stability and pricing in Naor's model with arrival rate uncertainty. [Doctoral Dissertation]. University of Texas – Austin; 2019. Available from: http://dx.doi.org/10.26153/tsw/2856
. 
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