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Title: Application of Blockchain and Artificial Intelligence Methods in Power System Operation and Control
Creator: Farhoumandi, Matin
Date: 2023
Description: The proliferation of distributed energy resources (DERs) and the large-scale electrification of transportation infrastructure are driving...
Show moreThe proliferation of distributed energy resources (DERs) and the large-scale electrification of transportation infrastructure are driving forces behind the ongoing evolution for transforming traditionally passive consumers into prosumers (both consumers and producers) in a coordinated system of power distribution network (PDN) and urban transportation network (UTN). In this new paradigm, peer-to-peer (P2P) energy trading is a promising energy management strategy for dynamically balancing the supply and demand in electricity markets. In this thesis, we propose the applications of artificial intelligence technology to power system operation and control. First, blockchain (BC) is applied to electric vehicle charging station (EVCS) operations to optimally transact energy in a hierarchical P2P framework. In the proposed framework, a decentralized privacy-preserving clearing mechanism is implemented in the transactive energy market (TEM) in which BC’s smart contracts are applied in a coordinated PDN and UTN operation. The effectiveness of the proposed TEM and its solution approach are validated via numerical simulations which are performed on a modified IEEE 123-bus PDN and a modified Sioux Falls UTN. Second, machine learning and deep learning methods are applied to short-term forecasting of non-conforming net load (STFNL). STFNL plays a vital role in enhancing the secure and efficient operation and control of power systems. However, power system consumption is affected by a variety of external factors and thus includes high levels of variations. These variations cause STFNL to be a challenging task as more DERs are integrated into the power grid. This thesis proposes two commonly used machine learning and deep learning methods, i.e., ensemble bagged and long short-term memory, for STFNL. The advantages, features and applications of these methods are expanded in a proposed fusion forecasting model that improves the STFNL accuracy. Additionally, data engineering and preprocessing options are used to increase the accuracy of the proposed fusion model. A comparative study based on practical load data is performed to demonstrate that the proposed fusion methodology can reach a relatively higher forecasting accuracy with lower error indices. Index Terms—Blockchain, deep learning and machine learning, electric vehicle charging stations, non-conforming net load forecasting, peer-to-peer transactive energy, power distribution and transportation networks, distributed energy resources, behind-the-meter supply resources.
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Title: Dynamic Risk and Dynamic Performance Measures Generated by Distortion Functions and Diversification Benefits Optimization
Creator: Liu, Hao
Date: 2023
Description: This thesis consists of two major parts, and it contributes to the fields of risk management and optimization.One contribution to risk...
Show moreThis thesis consists of two major parts, and it contributes to the fields of risk management and optimization.One contribution to risk management is made via developing dynamic risk measures and dynamic acceptability indices that can be characterized by distortion functions. In particular, we proved a representation theorem illustrating that the class of dynamic coherent risk measures generated by distortion functions coincides with a specific type of dynamic risk measures, the dynamic WV@R. We also investigate thoroughly various types of time consistencies for dynamic risk measures and dynamic acceptability indices in terms of distortion functions. Another contribution to risk management is proving strong consistency and asymptotic normality of two estimators of dynamic WV@R. In contrast to the exist- ing literature, our results do not rely on the assumptions of distribution of random variables. Instead, we investigate the asymptotic normality of estimators in terms of the generating distortion functions. Last but not least, we give counterexample to show that a sufficient condition of asymptotic normality is not necessary. The contribution to optimization is twofold. On the one hand, we formulate the (scalar) diversification optimization problem as a vector optimization problem (VOP), and show that a set-valued Bellman principle is satisfied by this VOP. On the other hand, we derive explicit policy gradient formula and implement the deep neural network to solve diversification optimization problem numerically. This deep learning technique allows to overcome computation difficulty caused by the non-convexity of VOP.
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Title: Toward a Network Model of Executive Functioning
Creator: Fuller, Jordan S.
Date: 2023
Description: The executive functions are the higher-order mental processes that are responsible for organized, strategic behavior. These functions have...
Show moreThe executive functions are the higher-order mental processes that are responsible for organized, strategic behavior. These functions have been a source of significant controversy since their initial introduction. This study sought to create a model of the executive functions utilizing psychological network analysis. Participants completed six measures reflecting inhibition, task switching, and working memory updating, as well as a fluid intelligence measure. A processing speed index was calculated from non-executive trials of various measures. Four networks were generated, including an executive functions network, an executive functions and intelligence network, an executive functions and processing speed network, and a network with all variables included. The resulting networks contained no stable edges between the executive functioning tasks. Stable edges were identified between the intelligence node and the two nodes reflecting working memory updating. There was an additional edge identified between processing speed and one measure of task switching. Results of the study may indicate that there is relative independence among executive functions. However, the management of task impurity in a psychological network analysis also merits further investigation.
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Title: Prediction and Control of In-Cylinder Processes in Heavy-Duty Engines Using Alternative Fuels
Creator: Pulpeiro Gonzalez, Jorge
Date: 2024
Description: This Ph.D. thesis focuses on advancing diagnostic techniques and control-oriented models to enhance the efficiency and performance of internal...
Show moreThis Ph.D. thesis focuses on advancing diagnostic techniques and control-oriented models to enhance the efficiency and performance of internal combustion (IC) engines, particularly heavy-duty engines utilizing alternative fuels. The research endeavors to contribute to the field of model-based control of engines through the development and implementation of innovative methodologies. The primary emphasis is on the development of diagnostic methods, control-oriented models and advanced control strategies for compression ignition engines using alternative fuels. The first key topic explores the determination of the Most Representative Cycle for Combustion Phasing Estimation based on cylinder pressure measurements. The method developed extracts crucial information from experimental data obtained from four distinct engines: the heavy-duty single-cylinder GCI engine, the light-duty multi-cylinder diesel engine, a CFR engine, and a single-cylinder light-duty Spark Ignition (SI) engine. This work lays the foundation for precise combustion phasing estimation, a critical parameter for engine control. The second major contribution involves the development of control-oriented models for Variable Geometry Turbochargers (VGT) and inter-coolers. Two models are established: a data-driven turbocharger model and an empirical inter-cooler model. These models are meticulously calibrated and validated using experimental data from a multi-cylinder light-duty diesel engine, providing valuable insights into the behavior of these components under varying conditions. The outcomes contribute to facilitate predictive control of engine air systems. The third core aspect of the thesis revolves around Model Predictive Control of Combustion Phasing in heavy-duty compression-ignition engines utilizing alternative fuels. A combustion phasing and engine load model is derived from experimental data and incorporated into an MPC framework. The MPC strategy is subsequently tested in the heavy-duty GCI test cell and compared against a conventional Proportional-Integral-Derivative (PID) control strategy. The results showcase the effectiveness of the MPC approach in achieving precise control of combustion phasing, demonstrating its potential for optimizing engine performance. In summary, this Ph.D. thesis contributes significantly to the field of engine controls by advancing diagnostic techniques, control-oriented models, and implementing a cutting-edge MPC-based control strategy for compression ignition engines using alternative fuels. The research findings not only enhance the understanding of in-cylinder processes but also pave the way for more efficient and sustainable heavy-duty engines using alternative fuels.
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Title: Capital Design: The Role of Design in Institutional Capital Allocation
Creator: Ostapchuk, Jordan
Date: 2024
Description: There is a paradox within the $100 trillion institutional investment industry: the more choices an institutional investor has, the more...
Show moreThere is a paradox within the $100 trillion institutional investment industry: the more choices an institutional investor has, the more challenging it becomes to make investment decisions. This paradox is significant because capital is one of the most transformational elements of the 21st century, driven by financialization, universal ownership, and increasing systemic risks. The direction of capital flows significantly influences the approach to addressing climate change, aging populations, and the transition to sustainable energy, in addition to supporting the essential physical and social infrastructure supported by institutional capital. This research proposes and substantiates a novel hypothesis: design can significantly influence capital allocation in institutional investment contexts. Through an institutional case study, expert interviews, workshops with master’s level design students, and systems-informed reflective practice, this research identifies asset classes as an important and changeable lens through which institutions engage with the future. It explores how these asset classes shape choices in the capital allocation process and identifies eight design capabilities particularly suited for institutional investment contexts. In doing so, it introduces a framework termed Capital Design. This framework illustrates how design can influence institutional capital allocation by integrating these design capabilities with investment tools through informational lenses within a choice/knowledge map. As a result, Capital Design offers an innovative approach for investors and investees to reorient toward emergent asset categories that directly meet the most urgent societal needs.
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Title: Gender Stereotype Biases Within Law Enforcement Clinical Psychological Evaluation
Creator: Porter, Maxwell G.
Date: 2023
Description: Female representation in law enforcement, despite significant improvements in past decades, remains markedly low at approximately 12%. One...
Show moreFemale representation in law enforcement, despite significant improvements in past decades, remains markedly low at approximately 12%. One possible barrier is the clinical psychological evaluation (CPE), a type of individualized assessment used to evaluate the mental or emotional fitness for duty of applicants. The present study examines the presence of potential gender bias in CPE for law enforcement positions by examining self-report personality assessment scores as well as narrative CPE recommendation reports generated by evaluating psychologists. Archival CPE data collected between 2014 through 2019 was obtained from a personnel selection consulting firm for entry-level law enforcement candidates (n = 390). Data included candidate scores on self-report psychological assessments (16PF, IPI-2), candidate background information, and psychologist-generated evaluation reports. A computer-aided text analysis using LIWC-22 was used to measure gender related inferences in the narrative report. Results indicated that (a) women received significantly lower assessor recommendation ratings than men, (b) significant differences in self-report personality scores were observed, however these were limited to a narrow subsection of traits, (c) gender was no longer a significant predictor of CPE outcome after controlling for applicant personality trait scores, and (d) meaningful differences in agency-related inferences in the narrative reports were observed, but it is unclear whether gender stereotypes influenced the reports. Practical implications, study limitations, and directions for future research are discussed.
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Title: Effect of Stress Triaxiality and Lode Angle on Ductile Fracture
Creator: Nia, Mahan
Date: 2023
Description: Although many ductile damage accumulation studies have been done in recent years, there is still insufficient research towards the development...
Show moreAlthough many ductile damage accumulation studies have been done in recent years, there is still insufficient research towards the development of ductile fracture models, mainly due to the difficulty of performing experiments under different states of multiaxial stress. The goals of this Ph.D. research are to (i) produce much-needed experimental data, (ii) investigate the performance of existing models against these data, and (iii) develop a new predictive ductile fracture model validated by experiments. The new model seeks to predict the fracture strain as a function of the stress triaxiality and normalized Lode angle. One of the prominent works in this area was done by Bai and Wierzbicki in 2008 by testing 2024-T351 aluminum alloy. They proposed an asymmetric 3D empirical fracture model with six model parameters. Thus, the Bai method was investigated alongside a new model for predicting ductile fracture. For that purpose, 2139-T8 aluminum alloy was chosen for our experimental program to evaluate these models better, and the data extracted from Bai's work was also used as an additional data set. An extensive experimental program was considered to create different stress states in the material, including tensile tests (with round smooth and four round notched and plate specimens), torsion, compression (with four smooth and two notched specimens), and shear-compression experiments (two different sizes). The specimens were longitudinally machined from a block of 2139-T8 aluminum alloy. The combined effects of two variables, stress triaxiality and normalized Lode angle, define a 3D fracture envelope for fracture strain. A parallel FE simulation (fine-tuned by the experimental results) has been performed for each experiment to evaluate the evolution of stress triaxiality and Lode angle in the gauge section of the specimens with complicated geometries. Finally, these results were used in developing two predictive fracture models. The first model is based on the Bai-Wierzbicki form of fracture. The second one is a new model that has been presented in this research. This new model is a modification of the Johnson-Cook fracture model and considers the simultaneous effects of Lode angle and stress triaxiality in fracture. The original Johnson-Cook fracture model (1984) does not consider the Lode angle effect. In the end, errors in the proposed approach to modeling ductile fracture have been compared to errors from Bai's work, resulting in the conclusions and recommendations for future studies.
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Title: Ground Monitors to Support Navigation Operations of ARAIM and GBAS
Creator: Patel, Jaymin Harshadkumar
Date: 2023
Description: Receiver Autonomous Integrity Monitoring (RAIM) currently provides safehorizontal navigation guidance to en route civil aircraft using the GPS...
Show moreReceiver Autonomous Integrity Monitoring (RAIM) currently provides safehorizontal navigation guidance to en route civil aircraft using the GPS L1 frequency. As an evolution of RAIM, Advanced RAIM (ARAIM) is being developed to provide vertical guidance in addition to horizontal using multiple constellations and dual frequency thus facilitating precision approach without ground support for civil aircraft. However, navigation guidance during zero-visibility (Category III) precision landing requires an additional support in real time from a Ground Based Augmentation System (GBAS). To improve the aircraft navigation solution, GBAS broadcasts a differential correction and monitors any failure on transmitted satellite signals. This dissertation contributes to ARAIM and GBAS to improve existing navigation operations in order to enable precision approach and landing.The achievable performance of ARAIM is highly dependent on the assumptionson a constellation’s nominal Signal-In-Space (SIS) error models and a priori fault probability. In the framework of ARAIM, an Integrity Support Message (ISM) is envisioned to carry the required SIS error-model parameters and fault statistics for users. The ISM is generated and validated through offline monitoring, and disseminated along the navigation message. The first dissertation contribution is to provide necessary satellite positions and clock biases as a truth product to evaluate nominal SIS range errors (SISREs). An estimator is developed to generate accurate ephemeris parameters to provide these truth products. The estimator’s performance is demonstrated for the Global Positioning System (GPS) constellation by utilizing the International GNSS Service (IGS) ground network to collect dual-frequency raw GPS code and carrier phase measurements. The resulting SISREs from the estimator are predicted to have a standard deviation of 0.5 m. When estimated ephemeris parameters and clock biases are compared with precise IGS orbit and clock products, the resulting SISREs are within ±2! at all times. In the second contribution, a new approach is proposed to generate the ISM by modeling the ephemeris parameter errors directly. In preliminary analysis, an ephemeris parameter error model is developed for the broadcast GPS legacy navigation message (LNAV) under nominal conditions. Then, the proposed approach is demonstrated to provide the nominal bias and standard deviation on GPS SISREs.As a part of fault monitoring in the GBAS, a ground monitor is developedto detect ephemeris failures, incorrect broadcast satellite positions, and hazardous ionosphere storms using either single- or dual frequency. The monitor also addresses the challenge of fault-free differential correction when satellites are rising, newly acquired, and re-acquired. The monitor utilizes differential code and carrier phase measurements across multiple reference receiver antennas as the basis for detection. Finally, the analytical performance of the monitor is demonstrated to meet Category III precision approach and landing requirements.
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Title: Characterization of Novel Concrete Formulations: High-Volume Fly Ash for Precast Industry Use and Non-Proprietary UHPC
Creator: Ordillas, Kurt Andrew
Date: 2024
Description: The use of high-volume fly ash concretes can be challenging for high-early strength applications, such as in precast construction, largely due...
Show moreThe use of high-volume fly ash concretes can be challenging for high-early strength applications, such as in precast construction, largely due to potential delays in strength gain resulting from relatively lower heats of hydration of the underlying binder formulations. Considering that the use of higher levels of available fresh or landfilled fly ash as a replacement for traditional ordinary Portland cement (OPC) could result in more sustainable mix designs, a framework to develop novel, high-volume fly ash mixes with optimized dosages of commercial grade gypsum and accelerating admixtures to enhance early-age strength performance. Early-age mechanical properties such as compressive strength, modulus of rupture, and modulus of elasticity were evaluated starting within 24 hours of specimen preparation. Experimental test results were then characterized and subsequently analyzed relative to current design provisions to highlight the best performing trial mixes (with respect to the early-age strength target) and cases where current design provisions are either unconservative or overly-conservative with respect to the test data. Additionally, thermal properties of concrete produced with fly ash were tested with two different curing environments, along with using code equations to determine if high volume fly ash provides a higher thermal resistance compared to OPC concrete. Wrapping up cementitious replacement with non-proprietary ultra high-performance concrete (UHPC) for transportation structures. Then reproducing mixtures to ensure target compressive strength values could be reached. Followed by increasing batch size to a larger quantity using a large mixer to create full-size specimens.
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Title: Exploring the role of perceived trustworthiness on leader humility's effectiveness
Creator: Pickett, Meghan L.
Date: 2024
Description: Over the last decade, several studies have uncovered the value of leader humility; however, these findings fail to consider the contextual...
Show moreOver the last decade, several studies have uncovered the value of leader humility; however, these findings fail to consider the contextual factors that may alter when and how humility plays a role. The current study looks to bridge this gap, by exploring how the effectiveness of perceived leader humility on follower outcomes (i.e., state learning goal orientation, feedback seeking behaviors, and employee engagement) is contingent upon follower perceptions of the leader’s trustworthiness. Data was collected from 160 leader-follower dyads across a variety of industries, using a cross-sectional design. Results from the study reinforced earlier findings that leader humility is often associated with positive follower outcomes such as seeking more feedback and reporting a higher learning goal orientation; however, these results were contingent upon how trustworthy they perceived the leader to be. Additionally, the study found evidence that perceptions of leader trustworthiness were related to group-based differences (e.g., age, gender). Together, these findings serve as a reminder that studying leader behaviors in isolation often risks simplifying the complex reality most leader’s face when trying to implement leader behaviors and skills.
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Title: Evaluation of the Native Microbiota and Comparative Analysis of a Known Cronobacter Sakazakii and a Newly Isolated Bacillus Cereus Strain in Powdered Infant Formula
Creator: Patil, Sonali Prashant
Date: 2024
Description: There have been numerous reports of Powdered Infant Formula (PIF) recalls and outbreaks due to the absence of a kill step in the post...
Show moreThere have been numerous reports of Powdered Infant Formula (PIF) recalls and outbreaks due to the absence of a kill step in the post-pasteurization processing, improper handling pre and post processing and/or reconstitution, and lack of effective sanitization and cleaning of the food contact surfaces in the manufacturing facilities. The objectives of this present study were to 1) survey and identify background microflora in commercial PIF products through microbiological analysis, 16S rRNA, and whole genome sequencing (WGS); 2) compare the survival rate of a known Cronobacter sakazakii and a newly isolated Bacillus cereus DFPST-SP1 in PIF under a humidity level of 33 ± 3% over a period of 28 d; 3) examine the relative resistance of these two strains to thermal treatments at temperatures 40, 70, and 100 °C followed by storage at room temperature (RT) for 30 min; and 4) evaluate the bactericidal effect of 70% ethanol on the two artificially deposited bacterial strains on stainless steel (SS) and plastic coupons. Three biological trials were conducted for each study. To determine whether the increase, decrease, or difference in the bacterial populations and other parameters like water activity (aw) was statistically significant, a T-test was performed (p ≤ 0.05 was considered significant). Results of 16S rRNA sequencing revealed the presence of certain bacterial species in PIF, such as Lactococcus lactis, B. cereus, Listeria monocytogenes, Staphylococcus aureus, Salmonella enterica, etc. distributed across a relative abundance of >25%, <25%, and ≤3%. After the enrichment and isolation as per Bacteriological Analytical Manual (BAM), C. sakazakii or S. enterica were not detected, while colonies exhibiting a blue-green appearance resembling Listeria spp. and certain Bacillus spp. were subjected to WGS for species-level identification. The assembly_1 from formulation 1 was confirmed as B. cereus sequence type 2255 and was renamed as B. cereus DFPST-SP1 in the contribution of this thesis work. The storage study conducted on PIF inoculated with C. sakazakii and B. cereus DFPST-SP1 at 33% RH showed that there was 0.25-0.27 log CFU/g reduction towards the end of 28 d, but no significant difference was observed between the two strains. The thermal challenge study revealed that the newly isolated B. cereus strain and C. sakazakii used in this study were highly thermotolerant. Based on the sanitizer challenge study, 70% ethanol was significantly more effective in reducing populations of C. sakazakii as compared to B. cereus. Moreover, higher log reductions of C. sakazakii 587 populations on stainless steel coupons compared to plastic coupons were observed, indicating that bacteria adhere more tightly to plastic surfaces than stainless steel (SS). Overall, the findings of this study shed new light on bolstering the safety standards of PIF and highlight the need for improved cleaning and sanitization procedures within manufacturing facilities in order to ensure the safety of reconstituted PIF, thereby enhancing public health, particularly infants and neonates.
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Title: Validation of the Chinese Version of the Penn Inventory of Scrupulosity in a Chinese Student Sample
Creator: De Leonardis, Andrew J.
Date: 2023
Description: Scrupulosity, which is a subtype of obsessive-compulsive disorder (OCD) characterized by religious obsessions and compulsions, has been shown...
Show moreScrupulosity, which is a subtype of obsessive-compulsive disorder (OCD) characterized by religious obsessions and compulsions, has been shown to be particularly debilitating. The current research concerning scrupulosity has mainly examined Western populations. There is a paucity of literature examining scrupulosity in Eastern populations, and no literature to date examining scrupulosity in a Chinese sample. In order to examine scrupulosity in China, a valid measure of scrupulosity is needed. The current study validated a Chinese version of the main measurement of scrupulosity, the Penn Inventory of Scrupulosity - Revised (PIOS-R), in a Chinese student sample. The PIOS-R was translated both linguistically and conceptually in consultation with the Chinese research team. New items were added for review and possible inclusion. 577 university students in China completed an online survey with study measures. This sample was randomly split into two groups, a primary group for the initial Confirmatory Factor Analysis (CFA) and a hold-out sample for follow up analyses. The initial CFA of the PIOS-R found that none of the three models identified met all criteria for adequate fit. A follow up Exploratory Factor Analysis (EFA) was conducted on the same group and found a two-factor model with updated items. This model was successfully cross-validated using a CFA in the hold-out sample. These analyses yielded the Penn Inventory of Scrupulosity – Chinese Version (PIOS-C). Preliminary reliability and validity were established with this revised measure. Having an accurate and valid measure of scrupulosity for the Chinese population will assist in characterizing scrupulosity in China and developing or refining appropriate treatments.
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Title: Developing Advanced Materials for Carbon Dioxide Electroreduction to Value-Added Chemicals and Fuels
Creator: Esmaeilirad, Mohammadreza
Date: 2023
Description: Developing highly efficient electrocatalysts for the carbon dioxide reductionreaction (CO2RR) to value-added fuels and chemicals offers a...
Show moreDeveloping highly efficient electrocatalysts for the carbon dioxide reductionreaction (CO2RR) to value-added fuels and chemicals offers a feasible pathway for renewable energy storage and could help mitigate the ever-increasing carbon dioxide (CO2) emissions from human activities. Different catalysts are known to catalyze CO2RR in aqueous solutions. Most known catalysts are only capable of transferring 2 electrons with needed protons to CO2 producing either carbon monoxide (CO) or formic acid (HCOOH). Copper (Cu) is the only electrocatalytic material that converts CO2 into different types of hydrocarbon products. Additionally, owing to Cu’s natural abundance and low cost, it has been intensively studied for CO2RR for decades. However, the required high input energy (overpotential), low product selectivity towards valuable fuel products, and the lack of long-term stability remain major challenges for Cu-based catalysts. This work aims to develop new materials that produce hydrocarbons at lower overpotentials with higher rates and greater selectivity than current copper catalysts. By implementing a process referred to as the electrocatalyst discovery cycle iterations between predications, catalyst testing, and active site characterization allow for the rational design and discovery of new and improved electrocatalysts for CO2RR. This methodology led to the discovery of different heteroatomic catalysts as low overpotential catalysts for electroreduction of CO2 high energy density hydrocarbon products.
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Title: Transactive Energy Market for Electric Vehicle Charging Stations in Constrained Power and Transportation Networks
Creator: Affolabi, Larissa Arielle Sèfiath
Date: 2023
Description: In response to the urgent need for decarbonization, our society is actively working towards reducing carbon emissions across various sectors....
Show moreIn response to the urgent need for decarbonization, our society is actively working towards reducing carbon emissions across various sectors. These efforts have resulted in the widespread adoption of distributed energy resources (DERs) in the electricity sector and the widespread adoption of electric vehicles (EVs) in the transportation sector. The growing popularity of EVs has resulted in rapid growth of charging infrastructure to meet the increasing demand. Recently, combined efforts across those two sectors have gained popularity with the deployment of EV charging stations (EVCSs) with on-site DERs like solar photovoltaic and/or battery energy storage systems not only to defer or avoid the need for power distribution equipment upgrades but also to achieve more environmentally friendly outcomes in terms of decarbonization goals. To increase transportation electrification, we need to expand further the charging infrastructure. The key challenge lies in accelerating charging station deployment while ensuring the safe and efficient operation of the power distribution system where most of this new load will be concentrated. Numerous research efforts have been dedicated to the study of EVCSs, with a focus on either optimizing the pricing of charging services or addressing the energy management challenges from the perspective of system operators. While these aspects are crucial, it is essential to recognize the importance of attracting private sector stakeholders to invest in and support the expansion of the EVCS network. Relying solely on subsidies is insufficient to finance the necessary scale of EVCS deployment required to accelerate the widespread adoption of EVs. The increasing adoption of EVCSs integrated with on-site DERs highlights the potential for Transactive Energy Market (TEM) operations among EVCSs. However, unlike regular prosumers, EVCS operations are uniquely influenced by both the power distribution and the transportation networks. In light of this issue, this dissertation proposes several multi-agent frameworks that leverage on-site DERs at EVCSs to establish a secondary revenue stream through a TEM. This dissertation investigates the technical and economic aspects of these multi-agent frameworks. At its core, we propose two holistic frameworks to solve the energy management problem of EVCSs within a TEM environment. Modeled as independent profit-driven entities, each EVCS optimally schedules its operation based on the day-ahead traffic assignment problem solved by the traffic operator agent. For the TEM clearing process, we propose two distinct lines of approach. First, a centralized approach where a single entity assumes both the market operator and grid operator functions. This integrated approach streamlines the decision-making process and ensures coordinated operations between the market and the power grid. Second, a decentralized approach, where separate entities take on the roles of the market operator and grid operator, respectively. This decentralized structure allows for more flexibility and distributed decision-making within the TEM. Furthermore, in contrast to many TEM related studies that overlook the complexity of the power distribution system, we introduce a comprehensive three-phase unbalanced optimal power flow model. This model incorporates features such as network reconfiguration and tap changers, allowing for a more accurate representation and understanding of the power distribution system's operation. Various case studies are used to prove the effectiveness of our proposed lines of approach to EVCSs’ day-ahead energy management problem.
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Title: High-Entropy Stabilization as a Designing Tool for Li-Ion Electrodes
Creator: Bandeira Jovino Marques, Otavio Jose
Date: 2023
Description: High-Entropy oxides (HEOs) form a new class of materials where the configurational entropy plays the stabilizing role of multicomponent...
Show moreHigh-Entropy oxides (HEOs) form a new class of materials where the configurational entropy plays the stabilizing role of multicomponent systems at high temperatures. Recently, it raised much attention for energy storage applications, especially on Li-ion batteries, where the combination of several different elements in a single solid solution can synergistically act to overcome some of its main drawbacks, improving the battery’s performance. The entropy stabilization opens new boundaries on electrode’s design by increasing the compositional space available for different structures and compounds. Not long ago, the high-entropy oxide (Mg0.2Co0.2Ni0.2Cu0.2Zn0.2)O demonstrated a big potential as anode material in Li-ion batteries. Its high capacity and long cycling stability raised a lot of questions about the role of the transition metals in the conversion reaction, and the configurational entropy contribution to the electrochemical reaction, further supporting the electrode’s stability. In order to investigate the structural evolution, the role of the multicomponent oxides and structures on the battery’s performance, and the entropic contribution to the electrode’s stability, this research proposes a systematic and robust methodology around the (Mg0.2Co0.2Ni0.2Cu0.2Zn0.2)O high-entropy oxide (HEO). The project heavily relies on the EXAFS ability to determine the short-range structure and the chemical sensitivity to isolate the elemental contribution of the compound at different cycling and charging states. First, the role of different metallic cations on the electrochemical reaction mechanism of the HEO was analyzed by the change in local structure during different charging steps of a Li-ion battery (Chapter 3). Secondly, the entropy contribution and tunability effects on electrochemical performance were tested in a series of medium and high-entropy oxides derived from the seminal HEO. Mg, Co, Ni, Cu, and Zn were individually removed from the HEO’s composition at a time and tested as Li-ion electrode. Fe was also added to the HEO’s composition (HEO+Fe) in order to prove the tunability effects and entropy contribution (Chapter 4). Operando x-ray absorption spectroscopy (XAS) was used to capture the short lived phases and the transient nature of the conversion reaction, to explain the origins of the extra storage capacity encountered on entropy stabilized systems (Chapter 5). Finally, the role of the high-entropy oxide initial structure was investigated and compared, to check versatility of the elements that can be used on a high-entropy system (Chapter 6).
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Title: Using High-Pressure Reverse Osmosis Technique to Desalinate Produced Water
Creator: Dallalzadeh Atoufi, Hossein
Date: 2023
Description: This dissertation presents a comprehensive investigation into the use of high-pressure reverse osmosis (HPRO) to desalinate produced water (PW...
Show moreThis dissertation presents a comprehensive investigation into the use of high-pressure reverse osmosis (HPRO) to desalinate produced water (PW) in the oil and gas industry, with the aim of developing sustainable water management strategies. The study analyzes fouling mechanisms in HPRO desalination, revealing the applicability of Hermia's fouling mechanism to high-salinity waters and highlighting the negligible impact of complete pore blocking and standard pore blocking in crossflow reverse osmosis (RO) desalination. Furthermore, the research investigates ion transport through commercial polyamide thin film composite membranes using the solution-friction model, elucidating the influence of factors such as pressure, temperature, and crossflow velocity on the initial flux while minimal impact on steady-state flux is observed. The assessment of oil and gas waste discharge in water systems provides insights into potential environmental consequences, and the analysis of the behavior of per- and polyfluoroalkyl substances (PFAS) in contaminated sediments using passive sampling demonstrates the rapid uptake of shorter-chain PFAS compounds due to their lower sorption potential and faster diffusion rates. The dissertation contributes to the development of sustainable water management strategies, addressing the challenges of produced water treatment and environmental contamination in the oil and gas industry, and offers valuable information on fouling mechanisms, impacts of ion transport mechanisms, waste discharge and PFAS behavior, enabling optimized desalination processes, informed waste management practices, and a better understanding of environmental contamination issues.
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Title: Machine Learning (ML) for Extreme Weather Power Outage Forecasting in Power Distribution Networks
Creator: Bahrami, Anahita
Date: 2023
Description: The Midwest region experiences a diverse range of severe weather conditions throughout the year. During the warmer months, thunderstorms,...
Show moreThe Midwest region experiences a diverse range of severe weather conditions throughout the year. During the warmer months, thunderstorms, heavy rain, lightning, tornadoes, and high winds pose a threat, while the colder season brings ice storms, snowstorms, high winds, and sleet storms, all of which can cause significant damage to the environment, properties, transportation systems, and power grids. The average climate in the Midwest is influenced by factors such as latitude, solar input, water systems' typical positions and movements, topography, the Great Lakes, and human activities. The combination of these conditions during different seasons contributes to the development of various types of storms. Therefore, it is crucial to predict the impacts of such atmospheric events on distribution and transmission lines, enabling utilities to assess and implement preventive measures and strategies to minimize the economic losses associated with these disasters. Additionally, the accurate classification of storm modes through an automated system allows operators to study trends in relation to climate change and implement necessary strategies to ensure grid reliability and resilience.In recent years, a significant number of power outages have occurred due to extreme ice formation on transmission and distribution networks, posing a threat to the power grid's resilience and reliability. To prepare power providers for snowstorms, extensive research has been conducted on snow accretion on power lines. Over the past two decades, many scientists have turned to machine learning (ML) algorithms for predicting ice accretion on overhead conductors, as ML models demonstrate superior accuracy compared to statistical forecasting models when it comes to forecasting challenging and fine-grained problems. However, most existing models primarily focus on predicting ice formation on power lines and fail to forecast the resulting damage to the distribution network. Therefore, this project proposes a model for predicting power outages caused by snow and ice storms in the distribution network. The goal is to aid in the planning process for disaster response and ensure the resilience and reliability of the power grid. The proposed outage prediction model incorporates statistical and machine learning techniques, taking into account features related to weather conditions, storm events, and information about the power network feeders.
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