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- Title
- A Holistic Analysis of the Long-Term Challenges (&) Potential Benefits of the Green Roof, Solar PV Roofing, and GRIPV Roofing Markets in Orlando, Florida.
- Creator
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Kelly, Carolina, Tatari, Omer, Oloufa, Amr, Mayo, Talea, Zheng, Qipeng, University of Central Florida
- Abstract / Description
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Green roofs and roof-mounted solar PV arrays have a wide range of environmental and economic benefits, including significantly longer roof lifetimes, reductions in urban runoff, mitigation of the urban heat island (UHI) effect, reduced electricity demand and energy dependence, and/or reduced emissions of greenhouse gases (GHGs) and other harmful pollutants from the electricity generation sector. Consequently, green roofs and solar panels have both become increasingly popular worldwide, and...
Show moreGreen roofs and roof-mounted solar PV arrays have a wide range of environmental and economic benefits, including significantly longer roof lifetimes, reductions in urban runoff, mitigation of the urban heat island (UHI) effect, reduced electricity demand and energy dependence, and/or reduced emissions of greenhouse gases (GHGs) and other harmful pollutants from the electricity generation sector. Consequently, green roofs and solar panels have both become increasingly popular worldwide, and promising new research has emerged for their potential combination in Green Roof Integrated Photovoltaic (GRIPV) roofing applications. However, due to policy resistance, these alternatives still have marginal market shares in the U.S., while GRIPV research and development is still severely limited today. As a result, these options are not yet sufficiently widespread in the United States as to realize their full potential, particularly due to a variety of policy resistance effects with respect to each specific alternative. The steps in the System Dynamics (SD) methodology to be used in this study are summarized as follows. First, based on a comprehensive review of relevant literature, a causal loop diagram (CLD) will be drawn to provide a conceptual illustration of the modeled system. Second, based on the feedback relationships observed in this CLD, a stock-flow diagram (SFD) will be developed to form a quantitative model. Third, the modeled SFD will be tested thoroughly to ensure its structural and behavioral validity with respect to the modeled system in reality using whatever real world data is available. Fourth, different policy scenarios will be simulated within the model to evaluate their long-term effectiveness. Fifth, uncertainty analyses will be performed to evaluate the inherent uncertainties associated with the analyses in this study. Finally, the results observed for the analyses in this study and possible future research steps will be discussed and compared as appropriate.
Show less - Date Issued
- 2018
- Identifier
- CFE0007406, ucf:52741
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007406
- Title
- DEVELOPMENT OF DAILY, MONTHLY, INTER-ANNUAL, AND MEAN ANNUAL HYDROLOGICAL MODELS BASED ON A UNIFIED RUNOFF GENERATION FRAMEWORK.
- Creator
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Kheimi, Marwan, Wang, Dingbao, Wahl, Thomas, Singh, Arvind, Zheng, Qipeng, University of Central Florida
- Abstract / Description
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The main goal of this dissertation develops a unified model structure for runoff generation based on observations from a large number of catchments. Furthermore, obtaining a comprehensive understanding of the physical controlling factors that control daily, monthly, and annual water balance models. Meanwhile, applying the developed Unified model on different climate conditions, and comparing it with different well-known models.The proposed model was compared with a similar timescale model ...
Show moreThe main goal of this dissertation develops a unified model structure for runoff generation based on observations from a large number of catchments. Furthermore, obtaining a comprehensive understanding of the physical controlling factors that control daily, monthly, and annual water balance models. Meanwhile, applying the developed Unified model on different climate conditions, and comparing it with different well-known models.The proposed model was compared with a similar timescale model (HyMOD, and abcd) and applied on 92 catchments from MOPEX dataset across the United States. The HyMOD and abcd are a well-known daily and monthly hydrological model used on a variety of researchers. The differences between the new model and HyMOD, and abcd include 1) the distribution function for soil water storage capacity is different and the new distribution function leads to the SCS curve number method; and 2) the computation of evaporation is also based on the distribution function considering the spatial variability of available water evaporation. The performance of all models along with parameters used is examined to understand the controlling factors. The generated results were calibrated and validated using the Nash-Sutcliffe efficiency coefficient (NSE), indicating that the Unified model has a moderate better performance against the HyMOD at a daily time scale, and abcd model at a monthly timescale. The proposed model using the SCS-CN method shows the effect of improving the performance.
Show less - Date Issued
- 2019
- Identifier
- CFE0007478, ucf:52684
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007478
- Title
- Data Mining Models for Tackling High Dimensional Datasets and Outliers.
- Creator
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Panagopoulos, Orestis, Xanthopoulos, Petros, Rabelo, Luis, Zheng, Qipeng, Dechev, Damian, University of Central Florida
- Abstract / Description
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High dimensional data and the presence of outliers in data each pose a serious challenge in supervised learning.Datasets with significantly larger number of features compared to samples arise in various areas, including business analytics and biomedical applications. Such datasets pose a serious challenge to standard statistical methods and render many existing classification techniques impractical. The generalization ability of many classification algorithms is compromised due to the so...
Show moreHigh dimensional data and the presence of outliers in data each pose a serious challenge in supervised learning.Datasets with significantly larger number of features compared to samples arise in various areas, including business analytics and biomedical applications. Such datasets pose a serious challenge to standard statistical methods and render many existing classification techniques impractical. The generalization ability of many classification algorithms is compromised due to the so-called curse of dimensionality. A new binary classification method called constrained subspace classifier (CSC) is proposed for such high dimensional datasets. CSC improves on an earlier proposed classification method called local subspace classifier (LSC) by accounting for the relative angle between subspaces while approximating the classes with individual subspaces. CSC is formulated as an optimization problem and can be solved by an efficient alternating optimization technique. Classification performance is tested in publicly available datasets. The improvement in classification accuracy over LSC shows the importance of considering the relative angle between the subspaces while approximating the classes. Additionally, CSC appears to be a robust classifier, compared to traditional two step methods that perform feature selection and classification in two distinct steps.Outliers can be present in real world datasets due to noise or measurement errors. The presence of outliers can affect the training phase of machine learning algorithms, leading to over-fitting which results in poor generalization ability. A new regression method called relaxed support vector regression (RSVR) is proposed for such datasets. RSVR is based on the concept of constraint relaxation which leads to increased robustness in datasets with outliers. RSVR is formulated using both linear and quadratic loss functions. Numerical experiments on benchmark datasets and computational comparisons with other popular regression methods depict the behavior of our proposed method. RSVR achieves better overall performance than support vector regression (SVR) in measures such as RMSE and $R^2_{adj}$ while being on par with other state-of-the-art regression methods such as robust regression (RR). Additionally, RSVR provides robustness for higher dimensional datasets which is a limitation of RR, the robust equivalent of ordinary least squares regression. Moreover, RSVR can be used on datasets that contain varying levels of noise.Lastly, we present a new novelty detection model called relaxed one-class support vector machines (ROSVMs) that deals with the problem of one-class classification in the presence of outliers.
Show less - Date Issued
- 2016
- Identifier
- CFE0006698, ucf:51920
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006698
- Title
- A Comprehensive Assessment of Vehicle-to-Grid Systems and Their Impact to the Sustainability of Current Energy and Water Nexus.
- Creator
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Zhao, Yang, Tatari, Omer, Oloufa, Amr, Mayo, Talea, Zheng, Qipeng, University of Central Florida
- Abstract / Description
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This dissertation aims to explore the feasibility of incorporating electric vehicles into the electric power grid and develop a comprehensive assessment framework to predict and evaluate the life cycle environmental, economic and social impact of the integration of Vehicle-to-Grid systems and the transportation-water-energy nexus. Based on the fact that electric vehicles of different classes have been widely adopted by both fleet operators and individual car owners, the following questions...
Show moreThis dissertation aims to explore the feasibility of incorporating electric vehicles into the electric power grid and develop a comprehensive assessment framework to predict and evaluate the life cycle environmental, economic and social impact of the integration of Vehicle-to-Grid systems and the transportation-water-energy nexus. Based on the fact that electric vehicles of different classes have been widely adopted by both fleet operators and individual car owners, the following questions are investigated: 1. Will the life cycle environmental impacts due to vehicle operation be reduced? 2. Will the implementation of Vehicle-to-Grid systems bring environmental and economic benefits? 3. Will there be any form of air emission impact if large amounts of electric vehicles are adopted in a short time? 4. What is the role of the Vehicle-to-Grid system in the transportation-water-energy nexus? To answer these questions: First, the life cycle environmental impacts of medium-duty trucks in commercial delivery fleets are analyzed. Second, the operation mechanism of Vehicle-to-Grid technologies in association with charging and discharging of electric vehicles is researched. Third, the feasible Vehicle-to-Grid system is further studied taking into consideration the spatial and temporal variance as well as other uncertainties within the system. Then, a comparison of greenhouse gas emission mitigation of the Vehicle-to-Grid system and the additional emissions caused by electric vehicle charging through marginal electricity is analyzed. Finally, the impact of the Vehicle-to-Grid system in the transportation-water-energy nexus, and the underlying environmental, economic and social relationships are simulated through system dynamic modeling. The results provide holistic evaluations and spatial and temporal projections of electric vehicles, Vehicle-to-Grid systems, wind power integration, and the transportation-water-energy nexus.
Show less - Date Issued
- 2017
- Identifier
- CFE0007300, ucf:52153
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007300
- Title
- Design, Synthesis and Characterization of Biomimetic, Bioinspired and Bio-related Functional Polymers for Atmospheric Water Recovery.
- Creator
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Alqassar, Abdullah, Chang, Ni-bin, Leon, Lorraine, Zheng, Qipeng, University of Central Florida
- Abstract / Description
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Atmospheric water recovery in changing environments has received wide attention in environmental science and engineering communities due to rapid population growth and frequent droughts. This study is focused on the design, synthesis, and characterization of biomimetic, bioinspired, and bio-related functional polymers (b3p) to help resolve the water supply issue especially in arid or semi-arid regions. It is aimed to develop unique synthetic methods to access well-defined polymers with the...
Show moreAtmospheric water recovery in changing environments has received wide attention in environmental science and engineering communities due to rapid population growth and frequent droughts. This study is focused on the design, synthesis, and characterization of biomimetic, bioinspired, and bio-related functional polymers (b3p) to help resolve the water supply issue especially in arid or semi-arid regions. It is aimed to develop unique synthetic methods to access well-defined polymers with the aid of nanomaterials and metal to produce next generation polymer materials for better atmospheric water recovery. The design of such new b3p is bioinspired by some skin materials of biological species such as frogs, beetles, or spiders. Such synthetic efforts are also coupled with fundamental studies of the polymer functions and structures, providing renewed understanding of how molecular structure and processing parameters associated with different nanomaterials impact macroscopic properties. This research was conducted by using a class of cross-linked hydrophilic copolymers known as hydrogels that exhibit a high fluid absorbency, up to 1,000 times to their own weight. Using free radical polymerization to cross-link two different monomers, such as Acrylamide (Am) and Acrylic Acid (Aa) loaded with Calcium Chloride (CaCl2) and coated with gold nanoparticles (Au-Np's), can produce novel thermally-responsive hydrophilic copolymer (e.g. Poly (Am-co-Aa)/Au-Np's/CaCl2) that was placed inside a controlled structure for testing. The new b3p materials can adsorb water vapor in the evening via a swelling process and discharge water vapor in the morning via a deswelling process to harvest the atmospheric water for recovery and reuse. The new b3p materials demonstrated high average swelling percentage of about 3541% when placed in water under a temperature range of [20-30oC] for 5 hours. The hydrogel loaded with 3.3701ivgrams CaCl2 was placed in the furnace under relative humidity percentage (RH) range of [80-90%] and can absorb up to 27% of the atmospheric water undergoing the same time. The research concludes that the proposed synthetic method contributes to solving such contemporary challenge in green chemistry to some extent. Further studies are needed to deeply investigate the ability of this new hydrogel to load more dissolved solids such as CaCl2.
Show less - Date Issued
- 2019
- Identifier
- CFE0007776, ucf:52370
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007776
- Title
- Methods for online feature selection for classification problems.
- Creator
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Razmjoo, Alaleh, Zheng, Qipeng, Rabelo, Luis, Boginski, Vladimir, Xanthopoulos, Petros, University of Central Florida
- Abstract / Description
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Online learning is a growing branch of machine learning which allows all traditional data miningtechniques to be applied on an online stream of data in real-time. In this dissertation, we presentthree efficient algorithms for feature ranking in online classification problems. Each of the methodsare tailored to work well with different types of classification tasks and have different advantages.The reason for this variety of algorithms is that like other machine learning solutions, there is...
Show moreOnline learning is a growing branch of machine learning which allows all traditional data miningtechniques to be applied on an online stream of data in real-time. In this dissertation, we presentthree efficient algorithms for feature ranking in online classification problems. Each of the methodsare tailored to work well with different types of classification tasks and have different advantages.The reason for this variety of algorithms is that like other machine learning solutions, there is usuallyno algorithm which works well for all types of tasks. The first method, is an online sensitivitybased feature ranking (SFR) which is updated incrementally, and is designed for classificationtasks with continuous features. We take advantage of the concept of global sensitivity and rankfeatures based on their impact on the outcome of the classification model. In the feature selectionpart, we use a two-stage filtering method in order to first eliminate highly correlated and redundantfeatures and then eliminate irrelevant features in the second stage. One important advantage of ouralgorithm is its generality, which means the method works for correlated feature spaces withoutpreprocessing. It can be implemented along with any single-pass online classification method withseparating hyperplane such as SVMs. In the second method, with help of probability theory wepropose an algorithm which measures the importance of the features by observing the changes inlabel prediction in case of feature substitution. A non-parametric version of the proposed methodis presented to eliminate the distribution type assumptions. These methods are application to alldata types including mixed feature spaces. At last, we present a class-based feature importanceranking method which evaluates the importance of each feature for each class, these sub-rankingsare further exploited to train an ensemble of classifiers. The proposed methods will be thoroughlytested using benchmark datasets and the results will be discussed in the last chapter.
Show less - Date Issued
- 2018
- Identifier
- CFE0007584, ucf:52567
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007584
- Title
- A Methodology for Data-Driven Decision-Making in Last Mile Delivery Operations.
- Creator
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Gutierrez Franco, Edgar, Rabelo, Luis, Karwowski, Waldemar, Zheng, Qipeng, Sarmiento, Alfonso, University of Central Florida
- Abstract / Description
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Across all industries, from manufacturing to services, decision-makers must deal day to day with the outcomes from past and current decisions that affect their business. Last-mile delivery is the term used in supply chain management to describe the movement of goods from a hub to final destinations. This research proposes a methodology that supports decision making for the execution of last-mile delivery operations in a supply chain. This methodology offers diverse, hybrid, and complementary...
Show moreAcross all industries, from manufacturing to services, decision-makers must deal day to day with the outcomes from past and current decisions that affect their business. Last-mile delivery is the term used in supply chain management to describe the movement of goods from a hub to final destinations. This research proposes a methodology that supports decision making for the execution of last-mile delivery operations in a supply chain. This methodology offers diverse, hybrid, and complementary techniques (e.g., optimization, simulation, machine learning, and geographic information systems) to understand last-mile delivery operations through data-driven decision-making. The hybrid modeling might create better warning systems and support the delivery stage in a supply chain. The methodology proposes self-learning procedures to iteratively test and adjust the gaps between the expected and real performance. This methodology supports the process of making effective decisions promptly, optimization, simulation, and machine learning models are used to support execution processes and adjust plans according to changes in conditions, circumstances, and critical factors. This research is applied in two case studies. The first one is in maritime logistics, which discusses the decision process to find the type of vessels and routes to deliver petroleum from ships to villages. The second is in city logistics, where a network of stakeholders during the city distribution process is analyzed, showing the potential benefits of this methodology, especially in metropolitan areas. Potential applications of this system will leverage growing technological trends (e.g., machine learning in supply chain management and logistics, internet of things). The main research impact is the design and implementation of a methodology, which can support real-time decisions and adjust last-mile operations depending on the circumstances. The methodology allows taking decisions under conditions of stakeholder behavior patterns like vehicle drivers, customers, locations, and traffic. As the main benefit is the possibility to predict future scenarios and plan strategies for the most likely situations in last-mile delivery. This will help determine and support the accurate calculation of performance indicators. The research brings a unified methodology, where different solution approaches can be used in a synchronized form, which allows researches and other interested people to see the connection between techniques. With this research, it was possible to bring advanced technologies in routing practices and algorithms to decrease operating cost and leverage the use of offline and online information, thanks to connected sensors to support decisions.
Show less - Date Issued
- 2019
- Identifier
- CFE0007645, ucf:52505
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007645
- Title
- Hybrid life cycle sustainability assessment-based multi-objective optimization: A case for sustainable transit bus fleet mix.
- Creator
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Sen, Burak, Zheng, Qipeng, Elshennawy, Ahmad, Tatari, Omer, University of Central Florida
- Abstract / Description
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Sustainable transportation idea includes not only switching from conventional energy sources to alternative fuel resources, but also diverging from private vehicle use and shifting to alternative transportation modes. As a part of alternative transportation mode, utilizing alternative fuels in public transportation operation supports sustainable transportation at it full-glance. Given their implications in terms of air quality and sustainable movement of people, transit buses, which provide...
Show moreSustainable transportation idea includes not only switching from conventional energy sources to alternative fuel resources, but also diverging from private vehicle use and shifting to alternative transportation modes. As a part of alternative transportation mode, utilizing alternative fuels in public transportation operation supports sustainable transportation at it full-glance. Given their implications in terms of air quality and sustainable movement of people, transit buses, which provide the primary public transportation service, are considered an ideal domain for the deployment of alternative fuels. An input-output (IO) model is developed based on Eora database (-) a detailed IO database that consists of national IO tables. Using the Eora-based IO model, this study quantifies and assesses the environmental, economic, and social impacts of alternative fuel buses in Atlanta, GA, and Miami, FL based on 6 macro-level sustainability indicators. The life cycle sustainability performance of these buses are then compared to that of a diesel transit bus as well as a regional comparison is carried out based on the two U.S. metropolitan areas. Based on these results, a multi-objective optimization model is constructed to find an optimal transit bus fleet for the studied U.S. regions. It has been found that a transit fleet that is composed of diesel buses operating in Atlanta has 30% more global warming potential than that of a transit fleet operating in Miami. The same bus fleet operating in Atlanta incurs a life cycle cost (LCC) that is more than double the LCC of the fleet operating in Miami. The study presents a way in which transit agencies can strategize their transitioning to a sustainable bus fleet.
Show less - Date Issued
- 2019
- Identifier
- CFE0007528, ucf:52620
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007528
- Title
- Optimization Approaches for Electricity Generation Expansion Planning Under Uncertainty.
- Creator
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Zhan, Yiduo, Zheng, Qipeng, Vela, Adan, Garibay, Ivan, Sun, Wei, University of Central Florida
- Abstract / Description
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In this dissertation, we study the long-term electricity infrastructure investment planning problems in the electrical power system. These long-term capacity expansion planning problems aim at making the most effective and efficient investment decisions on both thermal and wind power generation units. One of our research focuses are uncertainty modeling in these long-term decision-making problems in power systems, because power systems' infrastructures require a large amount of investments,...
Show moreIn this dissertation, we study the long-term electricity infrastructure investment planning problems in the electrical power system. These long-term capacity expansion planning problems aim at making the most effective and efficient investment decisions on both thermal and wind power generation units. One of our research focuses are uncertainty modeling in these long-term decision-making problems in power systems, because power systems' infrastructures require a large amount of investments, and need to stay in operation for a long time and accommodate many different scenarios in the future. The uncertainties we are addressing in this dissertation mainly include demands, electricity prices, investment and maintenance costs of power generation units. To address these future uncertainties in the decision-making process, this dissertation adopts two different optimization approaches: decision-dependent stochastic programming and adaptive robust optimization. In the decision-dependent stochastic programming approach, we consider the electricity prices and generation units' investment and maintenance costs being endogenous uncertainties, and then design probability distribution functions of decision variables and input parameters based on well-established econometric theories, such as the discrete-choice theory and the economy-of-scale mechanism. In the adaptive robust optimization approach, we focus on finding the multistage adaptive robust solutions using affine policies while considering uncertain intervals of future demands.This dissertation mainly includes three research projects. The study of each project consists of two main parts, the formulation of its mathematical model and the development of solution algorithms for the model. This first problem concerns a large-scale investment problem on both thermal and wind power generation from an integrated angle without modeling all operational details. In this problem, we take a multistage decision-dependent stochastic programming approach while assuming uncertain electricity prices. We use a quasi-exact solution approach to solve this multistage stochastic nonlinear program. Numerical results show both computational efficient of the solutions approach and benefits of using our decision-dependent model over traditional stochastic programming models. The second problem concerns the long-term investment planning with detailed models of real-time operations. We also take a multistage decision-dependent stochastic programming approach to address endogenous uncertainties such as generation units' investment and maintenance costs. However, the detailed modeling of operations makes the problem a bilevel optimization problem. We then transform it to a Mathematic Program with Equilibrium Constraints (MPEC) problem. We design an efficient algorithm based on Dantzig-Wolfe decomposition to solve this multistage stochastic MPEC problem. The last problem concerns a multistage adaptive investment planning problem while considering uncertain future demand at various locations. To solve this multi-level optimization problem, we take advantage of affine policies to transform it to a single-level optimization problem. Our numerical examples show the benefits of using this multistage adaptive robust planning model over both traditional stochastic programming and single-level robust optimization approaches. Based on numerical studies in the three projects, we conclude that our approaches provide effective and efficient modeling and computational tools for advanced power systems' expansion planning.
Show less - Date Issued
- 2016
- Identifier
- CFE0006676, ucf:51248
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006676
- Title
- Propagation of Unit Location Uncertainty in Dense Storage Environments.
- Creator
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Reilly, Patrick, Pazour, Jennifer, Zheng, Qipeng, Schneider, Kellie, University of Central Florida
- Abstract / Description
-
Effective space utilization is an important consideration in logistics systems and is especially important in dense storage environments. Dense storage systems provide high-space utilization; however, because not all items are immediately accessible, storage and retrieval operations often require shifting of other stored items in order to access the desired item, which results in item location uncertainty when asset tracking is insufficient. Given an initial certainty in item location, we use...
Show moreEffective space utilization is an important consideration in logistics systems and is especially important in dense storage environments. Dense storage systems provide high-space utilization; however, because not all items are immediately accessible, storage and retrieval operations often require shifting of other stored items in order to access the desired item, which results in item location uncertainty when asset tracking is insufficient. Given an initial certainty in item location, we use Markovian principles to quantify the growth of uncertainty as a function of retrieval requests and discover that the steady state probability distribution for any communicating class of storage locations approaches uniform. Using this result, an expected search time model is developed and applied to the systems analyzed. We also develop metrics that quantify and characterize uncertainty in item location to aid in understanding the nature of that uncertainty. By incorporating uncertainty into our logistics model and conducting numerical experiments, we gain valuable insights into the uncertainty problem such as the benefit of multiple item copies in reducing expected search time and the varied response to different retrieval policies in otherwise identical systems.
Show less - Date Issued
- 2015
- Identifier
- CFE0006052, ucf:50972
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006052
- Title
- A Multiagent Q-learning-based Restoration Algorithm for Resilient Distribution System Operation.
- Creator
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Hong, Jungseok, Sun, Wei, Zhou, Qun, Zheng, Qipeng, University of Central Florida
- Abstract / Description
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Natural disasters, human errors, and technical issues have caused disastrous blackouts to power systems and resulted in enormous economic losses. Moreover, distributed energy resources have been integrated into distribution systems, which bring extra uncertainty and challenges to system restoration. Therefore, the restoration of power distribution systems requires more efficient and effective methods to provide resilient operation.In the literature, using Q-learning and multiagent system (MAS...
Show moreNatural disasters, human errors, and technical issues have caused disastrous blackouts to power systems and resulted in enormous economic losses. Moreover, distributed energy resources have been integrated into distribution systems, which bring extra uncertainty and challenges to system restoration. Therefore, the restoration of power distribution systems requires more efficient and effective methods to provide resilient operation.In the literature, using Q-learning and multiagent system (MAS) to restore power systems has the limitation in real system application, without considering power system operation constraints. In order to adapt to system condition changes quickly, a restoration algorithm using Q-learning and MAS, together with the combination method and battery algorithm is proposed in this study. The developed algorithm considers voltage and current constraints while finding system switching configuration to maximize the load pick-up after faults happen to the given system. The algorithm consists of three parts. First, it finds switching configurations using Q-learning. Second, the combination algorithm works as a back-up plan in case of the solution from Q-learning violates system constraints. Third, the battery algorithm is applied to determine the charging or discharging schedule of battery systems. The obtained switching configuration provides restoration solutions without violating system constraints. Furthermore, the algorithm can adjust switching configurations after the restoration. For example, when renewable output changes, the algorithm provides an adjusted solution to avoid violating system constraints.The proposed algorithm has been tested in the modified IEEE 9-bus system using the real-time digital simulator. Simulation results demonstrate that the algorithm offers an efficient and effective restoration strategy for resilient distribution system operation.
Show less - Date Issued
- 2017
- Identifier
- CFE0006746, ucf:51856
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006746
- Title
- A framework for interoperability on the United States electric grid infrastructure.
- Creator
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Laval, Stuart, Rabelo, Luis, Zheng, Qipeng, Xanthopoulos, Petros, Ajayi, Richard, University of Central Florida
- Abstract / Description
-
Historically, the United States (US) electric grid has been a stable one-way power delivery infrastructure that supplies centrally-generated electricity to its predictably consuming demand. However, the US electric grid is now undergoing a huge transformation from a simple and static system to a complex and dynamic network, which is starting to interconnect intermittent distributed energy resources (DERs), portable electric vehicles (EVs), and load-altering home automation devices, that...
Show moreHistorically, the United States (US) electric grid has been a stable one-way power delivery infrastructure that supplies centrally-generated electricity to its predictably consuming demand. However, the US electric grid is now undergoing a huge transformation from a simple and static system to a complex and dynamic network, which is starting to interconnect intermittent distributed energy resources (DERs), portable electric vehicles (EVs), and load-altering home automation devices, that create bidirectional power flow or stochastic load behavior. In order for this grid of the future to effectively embrace the high penetration of these disruptive and fast-responding digital technologies without compromising its safety, reliability, and affordability, plug-and-play interoperability within the field area network must be enabled between operational technology (OT), information technology (IT), and telecommunication assets in order to seamlessly and securely integrate into the electric utility's operations and planning systems in a modular, flexible, and scalable fashion. This research proposes a potential approach to simplifying the translation and contextualization of operational data on the electric grid without being routed to the utility datacenter for a control decision. This methodology integrates modern software technology from other industries, along with utility industry-standard semantic models, to overcome information siloes and enable interoperability. By leveraging industrial engineering tools, a framework is also developed to help devise a reference architecture and use-case application process that is applied and validated at a US electric utility.
Show less - Date Issued
- 2015
- Identifier
- CFE0005647, ucf:50193
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0005647
- Title
- Inventory Management Problem for Cold Items with Environmental and Financial Considerations.
- Creator
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Hajiaghabozorgi, Ali, Pazour, Jennifer, Karwowski, Waldemar, Zheng, Qipeng, Nazzal, Dima, University of Central Florida
- Abstract / Description
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The overarching theme of this dissertation is analytically analyzing the cold supply chain from a financial and environmental perspective. Specifically, we develop inventory policy models in the cold supply chain that consider holding and transportation unit capacities. The models provide insights for the decision maker on the tradeoff between setting order quantities based on the cost or the emission function.In Chapter 2, we review two major bodies of literature: 1) supply chain design, and...
Show moreThe overarching theme of this dissertation is analytically analyzing the cold supply chain from a financial and environmental perspective. Specifically, we develop inventory policy models in the cold supply chain that consider holding and transportation unit capacities. The models provide insights for the decision maker on the tradeoff between setting order quantities based on the cost or the emission function.In Chapter 2, we review two major bodies of literature: 1) supply chain design, and 2) sustainability in supply chain design. We benefit from this literature review to map the current body of research on traditional supply chain for further comparison with the cold supply chain. Sustainability in supply chain network design is often measured by the carbon footprint; other sustainability metrics such as water footprint and sustainable energy are not included. Literature on supply chain design can be further broken down into its three major components: 1) facility location/allocation, 2) inventory management, and 3) facility location/allocation combined with inventory management. In Chapter 3, we study and present an overview of the cold chain. In accordance to the three levels of supply chain management decision making, the study is divided into the following three sections: (1) strategic level, (2) tactical level, and (3) operational level. Specifically, we capture how these decisions will impact the three main components of sustainability: economic, environmental, and social components. In addition, we explain how these components are different in the cold chain, in comparison to the traditional supply chain, and why such unique differences are worth studying. The intent of this chapter is to provide an overview of cold chains and to identify open areas for research. Examples from industrial cases, in addition to data and information from white papers, reports and research articles are provided.In Chapter 4, the cold item inventory problem is formulated as a single-period model that considers both financial and emissions functions. A new formulation for holding and transportation cost and emission is proposed by considering unit capacity for holding and transportation. This model applies to cold items that need to be stored at a certain, non-ambient temperature. Holding cold items in a warehouse is usually done by dividing the warehouse into a set of cold freezer units inside rather than refrigerating the entire warehouse. The advantage of such a design is that individual freezer units can be turned off to save cost and energy, when they are not needed. As a result, there is a fixed (setup) cost for holding a group of items, which results in a step function to represent the fixed cost of turning on the freezer units, in addition to the variable cost of holding items based on the number of units held in inventory. Three main goals of studying this problem are: 1) deriving the mathematical structure and modeling the holding and transportation costs and environmental functions in cold chains, 2) proposing exact solution procedures to solve the math models, and 3) analyzing the tradeoffs involved in making inventory decisions based on minimizing emissions vs. minimizing cost in cold chains.This problem demonstrates the tradeoff between the cost and the emission functions in an important supply chain decision. Also, the analytical models and solution approaches provide the decision maker with analytical tools for making better decisions.In Chapter 5, we expand the developed model from Chapter 4 to include multiple types of products. We consider a group of products that share capacities as a family of products. According to the problem formulation, we have two types of decision variables: (1) determining if a product is a member of a family or not, and (2) how much to order and how frequently to order for products within each family. We propose a solution procedure in accordance with the decision variable types: (1) a procedure for grouping (partitioning) the products into different families, and (2) a procedure to solve the inventory problem for each family. A set of experiments are designed to answer a number of research questions, and brings more understandings of the developed models and solutions algorithms.Finally, the conclusions of this dissertation and suggestions for future research topics are presented in Chapter 6.
Show less - Date Issued
- 2014
- Identifier
- CFE0005501, ucf:50365
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0005501
- Title
- Development and Application of an Optimization Approach for Cost-Effective Deployment of Advanced Wrong-Way Driving Countermeasures.
- Creator
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Sandt, Adrian, Al-Deek, Haitham, Eluru, Naveen, Hasan, Samiul, Zheng, Qipeng, University of Central Florida
- Abstract / Description
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Wrong-way driving (WWD) is a dangerous behavior, especially on high-speed divided highways. The nature of WWD crashes makes it difficult for agencies to combat them effectively. Advanced WWD countermeasures equipped with flashing lights, detection devices, and cameras can significantly reduce WWD. However, these countermeasures' high costs mean that agencies often cannot deploy them at all exit ramps. To help agencies identify the most cost-effective deployment locations for advanced WWD...
Show moreWrong-way driving (WWD) is a dangerous behavior, especially on high-speed divided highways. The nature of WWD crashes makes it difficult for agencies to combat them effectively. Advanced WWD countermeasures equipped with flashing lights, detection devices, and cameras can significantly reduce WWD. However, these countermeasures' high costs mean that agencies often cannot deploy them at all exit ramps. To help agencies identify the most cost-effective deployment locations for advanced WWD countermeasures, an innovative WWD countermeasure optimization approach was developed. This approach consists of a WWD hotspots model and a WWD countermeasures optimization algorithm. The WWD hotspots model uses non-crash WWD events, interchange designs, and traffic volumes to predict the number of WWD crashes on multi-exit roadway segments and identify hotspot segments with high WWD crash risk (WWCR). Then, the optimization algorithm uses these WWCR values to identify the optimal exits for advanced WWD countermeasure deployment based on available resources and other applicable constraints. This approach was applied to the Central Florida Expressway Authority (CFX) and Florida's Turnpike Enterprise (FTE) toll road networks. In both applications, the optimization algorithm provided significant WWCR reduction while meeting investment and other constraints and better allocated the agencies' resources compared to only deploying advanced WWD countermeasures in WWD hotspots. The optimization algorithm was also used to identify mainline sections on the CFX network with high WWCR. Additionally, the optimization algorithm was used to evaluate existing Rectangular Flashing Beacon (RFB) and Light-Emitting Diode (LED) advanced WWD countermeasures on the CFX (RFBs) and FTE (RFBs and LEDs) networks. These evaluations showed that the crash reduction and injury reduction benefits of these advanced WWD countermeasures have exceeded their costs since these countermeasures have been deployed. By using this WWD countermeasures optimization approach, agencies throughout the United States could proactively and cost-effectively deploy advanced WWD countermeasures to reduce WWD.
Show less - Date Issued
- 2018
- Identifier
- CFE0007364, ucf:52093
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007364
- Title
- Multi-level Optimization and Applications with Non-Traditional Game Theory.
- Creator
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Yun, Guanxiang, Zheng, Qipeng, Boginski, Vladimir, Karwowski, Waldemar, Yong, Jiongmin, University of Central Florida
- Abstract / Description
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We study multi-level optimization problem on energy system, transportation system and information network. We use the concept of boundedly rational user equilibrium (BRUE) to predict the behaviour of users in systems. By using multi-level optimization method with BRUE, we can help to operate the system work in a more efficient way. Based on the introducing of model with BRUE constraints, it will lead to the uncertainty to the optimization model. We generate the robust optimization as the...
Show moreWe study multi-level optimization problem on energy system, transportation system and information network. We use the concept of boundedly rational user equilibrium (BRUE) to predict the behaviour of users in systems. By using multi-level optimization method with BRUE, we can help to operate the system work in a more efficient way. Based on the introducing of model with BRUE constraints, it will lead to the uncertainty to the optimization model. We generate the robust optimization as the multi-level optimization model to consider for the pessimistic condition with uncertainty. This dissertation mainly includes four projects. Three of them use the pricing strategy as the first level optimization decision variable. In general, our models' first level's decision variables are the measures that we can control, but the second level's decision variables are users behaviours that can only be restricted within BRUE with uncertainty.
Show less - Date Issued
- 2019
- Identifier
- CFE0007881, ucf:52758
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007881
- Title
- Optical Properties of Single Nanoparticles and Two-dimensional Arrays of Plasmonic Nanostructures.
- Creator
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Zhou, Yadong, Zou, Shengli, Harper, James, Zhai, Lei, Chen, Gang, Zheng, Qipeng, University of Central Florida
- Abstract / Description
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The tunability of plasmonic properties of nanomaterials makes them promising in many applications such as molecular detection, spectroscopy techniques, solar energy materials, etc. In the thesis, we mainly focus on the interaction between light with single nanoparticles and two-dimensional plasmonic nanostructures using electrodynamic methods. The fundamental equations of electromagnetic theory: Maxwell's equations are revisited to solve the problems of light-matter interaction, particularly...
Show moreThe tunability of plasmonic properties of nanomaterials makes them promising in many applications such as molecular detection, spectroscopy techniques, solar energy materials, etc. In the thesis, we mainly focus on the interaction between light with single nanoparticles and two-dimensional plasmonic nanostructures using electrodynamic methods. The fundamental equations of electromagnetic theory: Maxwell's equations are revisited to solve the problems of light-matter interaction, particularly the interaction of light and noble nanomaterials, such as gold and silver. In Chapter 1, Stokes parameters that describe the polarization states of electromagnetic wave are presented. The scattering and absorption of a particle with an arbitrary shape are discussed. In Chapter 2, several computational methods for solving the optical response of nanomaterials when they are illuminated by incident light are studied, which include the Discrete Dipole Approximation (DDA) method, the coupled dipole (CD) method, etc. In Chapter 3, the failure and reexamination of the relation between the Raman enhancement factor and local enhanced electric field intensity is investigated by placing a molecular dipole in the vicinity of a silver rod. Using a silver rod and a molecular dipole, we demonstrate that the relation generated using a spherical nanoparticle cannot simply be applied to systems with particles of different shapes. In Chapter 4, a silver film with switchable total transmission/reflection is discussed. The film is composed of two-dimensional rectangular prisms. The factors affecting the transmission (reflection) as well as the mechanisms leading to the phenomena are studied. Later, in Chapter 5 and 6, the sandwiched nano-film composed of two 2D rectangular prisms arrays and two glass substrates with a continuous film in between is examined to enhance the transmission of the continuous silver film.
Show less - Date Issued
- 2018
- Identifier
- CFE0007117, ucf:51943
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007117
- Title
- Development of an Adaptive Restoration Tool For a Self-Healing Smart Grid.
- Creator
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Golshani, Amir, Sun, Wei, Qu, Zhihua, Vosoughi, Azadeh, Zhou, Qun, Zheng, Qipeng, University of Central Florida
- Abstract / Description
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Large power outages become more commonplace due to the increase in both frequency and strength of natural disasters and cyber-attacks. The outages and blackouts cost American industries and business billions of dollars and jeopardize the lives of hospital patients. The losses can be greatlyreduced with a fast, reliable and flexible restoration tool. Fast recovery and successfully adapting to extreme events are critical to build a resilient, and ultimately self-healing power grid. This...
Show moreLarge power outages become more commonplace due to the increase in both frequency and strength of natural disasters and cyber-attacks. The outages and blackouts cost American industries and business billions of dollars and jeopardize the lives of hospital patients. The losses can be greatlyreduced with a fast, reliable and flexible restoration tool. Fast recovery and successfully adapting to extreme events are critical to build a resilient, and ultimately self-healing power grid. This dissertation is aimed to tackle the challenging task of developing an adaptive restoration decisionsupport system (RDSS). The RDSS determines restoration actions both in planning and real-time phases and adapts to constantly changing system conditions. First, an efficient network partitioning approach is developed to provide initial conditions for RDSS by dividing large outage network into smaller islands. Then, the comprehensive formulation of RDSS integrates different recovery phases into one optimization problem, and encompasses practical constraints including AC powerflow, dynamic reserve, and dynamic behaviors of generators and load. Also, a frequency constrained load recovery module is proposed and integrated into the RDSS to determine the optimal location and amount of load pickup. Next, the proposed RDSS is applied to harness renewable energy sources and pumped-storage hydro (PSH) units by addressing the inherent variabilities and uncertainties of renewable and coordinating wind and PSH generators. A two-stage stochastic and robust optimization problem is formulated, and solved by the integer L-shaped and column-and-constraintsgeneration decomposition algorithms. The developed RDSS tool has been tested onthe modified IEEE 39-bus and IEEE 57-bus systems under different scenarios. Numerical results demonstrate the effectiveness and efficiency of the proposed RDSS. In case of contingencies or unexpected outages during the restoration process, RDSS can quickly update the restoration plan and adapt to changing system conditions. RDSS is an important step toward a self-healing power grid and its implementation will reduce the recovery time while maintaining system security.
Show less - Date Issued
- 2017
- Identifier
- CFE0007284, ucf:52169
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007284
- Title
- Performance Predication Model for Advance Traffic Control System (ATCS) using field data.
- Creator
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Mirza, Masood, Radwan, Essam, Abou-Senna, Hatem, Abdel-Aty, Mohamed, Zheng, Qipeng, University of Central Florida
- Abstract / Description
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Reductions in capital expenditure revenues have created greater demands from users for quality service from existing facilities at lower costs forcing agencies to evaluate the performance of projects in more comprehensive and "greener" ways. The use of Adaptive Traffic Controls Systems (ATCS) is a step in the right direction by enabling practitioners and engineers to develop and implement traffic optimization strategies to achieve greater capacity out of the existing systems by optimizing...
Show moreReductions in capital expenditure revenues have created greater demands from users for quality service from existing facilities at lower costs forcing agencies to evaluate the performance of projects in more comprehensive and "greener" ways. The use of Adaptive Traffic Controls Systems (ATCS) is a step in the right direction by enabling practitioners and engineers to develop and implement traffic optimization strategies to achieve greater capacity out of the existing systems by optimizing traffic signal based on real time traffic demands and flow pattern. However, the industry is lagging in developing modeling tools for the ATCS which can predict the changes in MOEs due to the changes in traffic flow (i.e. volume and/or travel direction) making it difficult for the practitioners to measure the magnitude of the impacts and to develop an appropriate mitigation strategy. The impetus of this research was to explore the potential of utilizing available data from the ATCS for developing prediction models for the critical MOEs and for the entire intersection. Firstly, extensive data collections efforts were initiated to collect data from the intersections in Marion County, Florida. The data collected included volume, geometry, signal operations, and performance for an extended period. Secondly, the field data was scrubbed using macros to develop a clean data set for model development. Thirdly, the prediction models for the MOEs (wait time and queue) for the critical movements were developed using General Linear Regression Modeling techniques and were based on Poisson distribution with log linear function. Finally, the models were validated using the data collected from the intersections within Orange County, Florida. Also, as a part of this research, an Intersection Performance Index (IPI) model, a LOS prediction model for the entire intersection, was developed. This model was based on the MOEs (wait time and queue) for the critical movements.In addition, IPI Thresholds and corresponding intersection capacity designations were developed to establish level of service at the intersection. The IPI values and thresholds were developed on the same principles as Intersection Capacity Utilization (ICU) procedures, tested, and validated against corresponding ICU values and corresponding ICU LOS.
Show less - Date Issued
- 2018
- Identifier
- CFE0007055, ucf:51975
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007055
- Title
- Warrants for Right-Turn Flashing Yellow Arrow Signal Phases.
- Creator
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Alfawzan, Mohammed, Radwan, Ahmed, Eluru, Naveen, Abou-Senna, Hatem, Zheng, Qipeng, University of Central Florida
- Abstract / Description
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The right-turn flashing yellow arrow (FYA) signal phasing is a new signal practice in the United States. The Manual on Uniform Traffic Control Devices MUTCD (2009) allocates a signal phasing section for the right-turn FYA, which requires a four-section head FYA signal. It supports multiple phases' indications that guide the motorist through permissive, protected, and/or permissive/protected phases. For this dissertation, I investigated three permissive right-turn FYA signal phases in various...
Show moreThe right-turn flashing yellow arrow (FYA) signal phasing is a new signal practice in the United States. The Manual on Uniform Traffic Control Devices MUTCD (2009) allocates a signal phasing section for the right-turn FYA, which requires a four-section head FYA signal. It supports multiple phases' indications that guide the motorist through permissive, protected, and/or permissive/protected phases. For this dissertation, I investigated three permissive right-turn FYA signal phases in various traffic conditions and signal timing circumstances. The first permissive right-turn FYA signal phase is the tight-turn on impeding through (RTOIT) taking place during the cross-street through traffic movement. The second permissive right-turn FYA signal phase occurs during the opposing left-turn approach movement and so is called the right-turn on impeding left (RTOIL). The third permissive right-turn phase is a right-turn on through green impeded only by the side street pedestrians called the right-turn on adjacent through (RTOAT). I aimed to develop warrants leading to efficient implementation of permissive right-turn FYA signal phases based on microsimulation analysis. I developed multinomial logit models to establish a decision support system that predicts the efficiency attributes of the permissive right-turn FYA signal phases.
Show less - Date Issued
- 2019
- Identifier
- CFE0007883, ucf:52801
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007883
- Title
- STOCHASTIC OPTIMIZATION AND APPLICATIONS WITH ENDOGENOUS UNCERTAINTIES VIA DISCRETE CHOICE MODELSl.
- Creator
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Chen, Mengnan, Zheng, Qipeng, Boginski, Vladimir, Vela, Adan, Yayla Kullu, Muge, University of Central Florida
- Abstract / Description
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Stochastic optimization is an optimization method that solves stochastic problems for minimizing or maximizing an objective function when there is randomness in the optimization process. In this dissertation, various stochastic optimization problems from the areas of Manufacturing, Health care, and Information Cascade are investigated in networks systems. These stochastic optimization problems aim to make plan for using existing resources to improve production efficiency, customer...
Show moreStochastic optimization is an optimization method that solves stochastic problems for minimizing or maximizing an objective function when there is randomness in the optimization process. In this dissertation, various stochastic optimization problems from the areas of Manufacturing, Health care, and Information Cascade are investigated in networks systems. These stochastic optimization problems aim to make plan for using existing resources to improve production efficiency, customer satisfaction, and information influence within limitation. Since the strategies are made for future planning, there are environmental uncertainties in the network systems. Sometimes, the environment may be changed due to the action of the decision maker. To handle this decision-dependent situation, the discrete choice model is applied to estimate the dynamic environment in the stochastic programming model. In the manufacturing project, production planning of lot allocation is performed to maximize the expected output within a limited time horizon. In the health care project, physician is allocated to different local clinics to maximize the patient utilization. In the information cascade project, seed selection of the source user helps the information holder to diffuse the message to target users using the independent cascade model to reach influence maximization. \parThe computation complexities of the three projects mentioned above grow exponentially by the network size. To solve the stochastic optimization problems of large-scale networks within a reasonable time, several problem-specific algorithms are designed for each project. In the manufacturing project, the sampling average approximation method is applied to reduce the scenario size. In the health care project, both the guided local search with gradient ascent and large neighborhood search with Tabu search are developed to approach the optimal solution. In the information cascade project, the myopic policy is used to separate stochastic programming by discrete time, and the Markov decision process is implemented in policy evaluation and updating.
Show less - Date Issued
- 2019
- Identifier
- CFE0007792, ucf:52347
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007792
