Doctor of Philosophy, University of Toledo, 2019, Electrical Engineering

The increasing complexity in the power grid, which is driven by distributed energy resources (DER) such as distributed generation, storage systems, and controllable loads, demands advanced control strategies for effective energy management. This dissertation demonstrates the development and implementation of two control strategies for managing DER. The first control objective is mitigating variability generated by PV output power using a battery energy storage system (BESS). The proposed method uses a novel adaptive moving average and adaptive state of charge (SoC) correction control method to achieve a better tradeoff between battery utilization and degree of PV power smoothness. The second control objective is achieving demand response, which refers to the mechanism that can shift the consumption of a load, such as the energy system in a building, to balance demand and supply of electricity, without harming the thermal comfort of the building's occupants and the free-will of the consumer. This is accomplished with PNNL's Intelligent Load Control (ILC), which manages the power consumption of loads based on priority criteria. ILC's unidirectional and bidirectional capability and their respective applications are demonstrated with the testbed. Additionally, this dissertation also explores the real-life implementation of these control strategies at the Scott Park campus (SPC) of the University of Toledo (UT). The SPC consists of eight buildings, a 1 MW solar array, and a 130-kWh BESS. For the dissertation work, a communication and control infrastructure has been created on the SPC electrical network by integrating an Internet of Things (IoT) system using Eclipse VOLTTRON. This infrastructure establishes communication and control between various devices on the SPC, which is used to validate and implement the proposed control strategies.

Committee: Raghav Khanna (Advisor); Mansoor Alam (Committee Member); Daniel Georgiev (Committee Member); Richard Molyet (Committee Member); Michael Heben (Committee Member) Subjects: Electrical Engineering; Energy

MS, University of Cincinnati, 2024, Engineering and Applied Science: Civil Engineering

Buildings are the largest energy consumers, contributing to more than 40% of global carbon dioxide (CO2) emissions [1]. A significant portion of this energy consumption is attributed to building mechanical systems, particularly air handling units. Air handling units are a crucial component responsible for distributing conditioned air throughout the building. The supply and return fans within these units play a key role in air circulation and are responsible for substantial energy usage. This paper investigates strategies to decrease the energy burden on these fans. An evaluation of existing economizer damper control measures highlighted a dire need for a novel approach to modulate outdoor, exhaust, and return air dampers. The “split-signal damper control” suggested by Nassif and Moujaes [2] showed promising results, even though it required improvements for effective implementation in building mechanical systems. Further investigations introduced a method known as “duct static pressure set point reset”, which involves dynamically adjusting duct static pressure according to space airflow requirements rather than maintaining a constant pressure set point [3]. This research aims to improve the economizer damper control sequence for implementation in variable air volume (VAV) systems, develop a statistical model to simulate energy savings and refine the split-signal damper control sequence by integrating demand control ventilation (DCV). Additionally, cumulate energy savings and cost reductions due to duct static pressure set point adjustments, and improved economizer damper control sequence to attract building owners and operation managers. Experimental tests conducted on chilled water VAV system yielded an energy savings of 0.2% to 5% on improved split-signal damper control compared to the traditional three-coupled damper control method. Additionally, the control sequence could prevent reverse airflow through the exhaust damper. The statistic (open full item for complete abstract)

Committee: Nabil Nassif Ph.D. (Committee Chair); Tianren Wu Ph.D. (Committee Member); Arpan Guha Ph.D. (Committee Member) Subjects: Civil Engineering

Doctor of Business Administration (D.B.A.), Franklin University, 2023, Business Administration

Foster parents in the State of Ohio were explored in this dissertation study. The purpose of this research was to address a gap in existing literature concerning foster parent experiences in Ohio within the foster care system. The job of a foster parent is to provide a safe place and to make a difference in the lives of children that come into their homes. This role is oftentimes accompanied by high demands, low autonomy, and low support. It is imperative to correct this pattern as it can result in adverse psychological effects and negative impacts on mental health, leading to foster parents exiting the system in high volumes. This study used a qualitative approach to address the research question. The Job Demand-Control-Support (JDCS) model was applied to this research and used as a guide in providing a deeper analysis of the inner workings of the foster care system as it pertains to foster parents. A purposeful and snowball sample of 20 Ohio foster parents participated in open-ended Zoom and telephone interviews. They provided in-depth responses on their experiences within the foster care system. The interviews were transcribed, coded, and analyzed for major themes. ATLAS.ti Cloud software was used for coding analysis of the collected data. Four major themes and nine sub-themes resulted from the interviews. The findings contributed to research by providing future foster parents and leadership within the foster care system with meaningful strategies to improve foster parent retention rates in Ohio.

Committee: Susan Campbell (Committee Chair); John Nadalin (Committee Member); Dail Fields (Committee Member) Subjects: Business Administration; Public Administration; Public Policy; Social Research; Social Structure

PhD, University of Cincinnati, 2021, Engineering and Applied Science: Civil Engineering

The primary energy sources in commercial buildings are electricity that accounts for 61%, followed by 32% for natural gas. According to EIA, the heating, ventilation, and air condition systems account for about 25% of the total commercial building's energy use in the US. Therefore, advanced modeling and optimization methods of the system components and operation offer great ways to reduce energy consumption. Since HVAC systems modeling is a characteristic and challenging process thus, while developing an HVAC system and component model, close attention should be given to the accuracy of the model structure, model parameters, and constraints. So, the final selected model can accurately deal with constraints, uncertainties, control the time-varying applications and handle a broad range of operating conditions. Also, the use of the optimization process to automate selecting the best model structure is crucial. Because every component is different, we cannot propose one model to fit that specified component in all systems. Choosing the best model structure is a time-consuming process. Here comes the optimization process role in automating the process of selecting the optimal model structure for each application. This research will introduce an innovative method of modeling and optimizing HVAC system operation to reduce the total energy consumption while improving the indoor thermal comfort level. The data-driven two-level optimization technique introduced in this research will utilize the use of real system performance data collected from the building automation systems (BAS) to create accurate component modeling and optimization process as the first level of optimization (MLO). Accurate component modeling techniques are crucial for the results accuracy of the process of optimization the HVAC systems performance. Lastly, artificial neural network (ANN) was chosen as the component modeling tool. The second level of optimization utilizes the whole system-level opt (open full item for complete abstract)

Committee: Nabil Nassif (Committee Chair); Hazem Elzarka Ph.D. (Committee Member); Amanda Webb (Committee Member); Munir Nazzal Ph.D. (Committee Member); Raj Manglik Ph.D. (Committee Member) Subjects: Engineering; Labor Economics; Theater

MA, Kent State University, 2017, College of Arts and Sciences / Department of Sociology and Criminology

The recent introduction of women into the special forces of the United States military has reignited the debate on how women will integrate into these previously all male roles. Drawing on the Job-Demand-Control-Support (JDCS) model and focusing on the support aspect of this model, this study examines the role of sex discrimination and sexual harassment, two commonly experienced actions by members of a gender-homogeneous team, on voluntary retention. The author tests five hypotheses and three gender-related sub-hypothesis concerning the effects of sex discrimination and sexual harassment on retention via perceived organizational support and workplace satisfaction. Multilevel structural equation models were produced using data from the public use version of the 2012 Workplace and Gender Relations Survey of Active Members. Finding show there is no significant gender differences in the direct effects of sex discrimination and sexual harassment on retention. However, there is a gender difference in the indirect paths of sex via perceived organizational support and workplace satisfaction. This study informs the JDCS model by suggesting that perceived organizational support is constructed and influences job-related outcome differently for males and females.

Committee: Timothy Owens (Advisor); Clare Stacey (Committee Member); Adrianne Frech (Committee Member) Subjects: Social Psychology; Sociology