Master of Science (M.S.), University of Dayton, 2017, Mechanical Engineering

The ability to forecast demand for large facilities will be increasingly important as real-time power pricing scenarios become increasingly present. Accurate prediction will inform data-driven power shedding to reduce energy costs most effectively with minimal sacrifice of comfort. A number of previous researchers have researched this topic, achieving results with varying amount of success. This study looks to forecast demand for a university complex of buildings, subject to the unique occupancy variation of such institutions. Specifically addressed is the use of academic institutional data associated with temporal enrollment and the academic calendar. As well, it addresses use of demand data in all buildings in an effort to more accurately predict this aggregate demand of the university. A data mining based approach based upon a Random Forest regression tree algorithm is used to develop the forecast model. The mean absolute percentage error (MAPE) value associated with the model applied to a validation set of data is on the order of 2.21 % based upon actual weather data. Using forecasted weather data, the MAPE increases to approximately 6.65 % in predicted day-ahead demand.

Committee: Kevin Hallinan (Committee Chair); Andrew Chiasson (Committee Member); Zhongmei Yao (Committee Member) Subjects: Artificial Intelligence; Climate Change; Energy; Engineering; Environmental Engineering; Mechanical Engineering; Statistics

Master of Science in Mechanical Engineering, Cleveland State University, 2022, Washkewicz College of Engineering

Building Energy Simulations (BES) are necessary for designing energy-efficient systems. Open-source simulation software developed by the Department of Energy (DOE), EnergyPlus (EP) provides Typical Meteorological Year (TMY) weather data that consists of a 15-year average. Two major concerns about this data are the inability to detect extreme conditions and limited data locations. There is a greater number of Microclimate (MC) stations that can be used for simulations, but it involves time-consuming data preparation to match the EP format. This study investigated the effects of Urban Heat Island (UHI) and the MC of Lake Erie. A comparison of the MC data to TMY data was performed by running heating and cooling calculations with each of the weather datasets in EP. UHI simulation for New York City Manhattan in July 2020 resulted in a 17.6% higher cooling demand than in the rural area, and 26.0% higher than the TMY data. Lake MC comparison found an almost 10.0% difference in July 2019' cooling demand between two stations located 20 miles apart. This research reassured that it is essential to include MC data in energy building design and found a way to eliminate the time-consuming aspect of it. With the help of Virtual Information Fabric Infrastructure (VIFI) MC data can be automatically prepared and converted to the correct format. Furthermore, a user-friendly portal that includes both TMY and MC weather is being developed to make accurate energy simulations highly accessible.

Committee: Yongxin Tao (Advisor); Wei Zhang (Committee Member); Maryam Younessi Sinaki (Committee Member) Subjects: Engineering; Mechanical Engineering

Doctor of Philosophy (Ph.D.), University of Dayton, 2020, Mechanical Engineering

This dissertation focusses mainly on loads determination, building informatics, and geothermal energy systems. The first chapter is Low-Energy Opportunity for Multi-Family Residences: A Simulation-Based Study of a Borehole Thermal Energy Storage System. In this chapter, we propose a district borehole thermal solar energy storage (BTES) system for both retrofit and new construction for a multi-family residence in the Midwestern United States, where the climate is moderately cold with very warm summers. Actual apartment interval power and water demand data was mined and used to estimate unit level hourly space and water heating demands, which was subsequently used to design a cost-optimal BTES system. Using a dynamic simulation model to predict the system performance over a 25-year period, a parametric study was conducted that varied the sizes of the BTES system and the solar collector array. A life-cycle cost analysis concluded that is it possible for an optimally-sized system to achieve an internal rate of return (IRR) of 11%, while reducing apartment-wide energy and carbon consumption by 46% The promise for district-scale adoption of BTES in multi-family residences is established, particularly for new buildings. In the second chapter (Alternate Approach to the Calculation of Thermal Response Factors for Vertical Borehole Ground Heat Exchanger Arrays Using an Incomplete Bessel Function), we presents another methodology for the calculation of dimensionless thermal response factors for vertical borehole ground heat exchanger (GHX) arrays, which is a concept introduced by Eskilson (1987). The presented method is based on a well-known solution to an analogous problem in the field of well hydraulics. This solution method, known mathematically as an incomplete Bessel function, and known in the field of well hydraulics as the `leaky aquifer function', describes the hydraulic head distribution in an aquifer with predominantly radial flow to a well combined with vertical (open full item for complete abstract)

Committee: Kevin P Hallinan Professor (Committee Chair); Andrew D. Chiasson Professor (Committee Member); Robert J. Brecha Professor (Committee Member); Robert B. Gilbert Professor (Committee Member) Subjects: Mechanical Engineering

Master of Science (M.S.), University of Dayton, 2017, Mechanical Engineering

The objective is to research the possibility of achieving 100% renewable energy for electricity demand and hot water for residences in seven areas in Ohio by utilizing three locally- available renewable energy sources. These areas are chosen because they are generally rural and thus can potentially provide biomass energy in the form of crop residue. Hourly electricity demand based on OpenEI data for this region is utilized to determine hourly renewable energy expected to meet hourly demand. The three renewable energy sources locations solar energy, wind energy, and biomass energy from residues located in the seven counties. Three alternatives are examined for proposed power plant configurations with sustainable power sources. These cases rely upon Biomass used to create power and hot water and either utilize all of the accessible biomass in the regions to generate electricity and waste heat or utilize biomass to provide heat for hot water or use biomass to meet remaining electricity demand after estimating the solar and wind power plant outputs. The levelized cost of energy in each scenario is calculated as well.

Committee: Robert Brecha Professor (Committee Chair); Robert Gilbert Professor (Committee Member); Andrew Chiasson Assistant Professor (Committee Member) Subjects: Electrical Engineering; Mechanical Engineering

Master of Science, University of Toledo, 2010, Electrical Engineering

Demand-Side Management (DSM) can be defined as the implementation of policies and measures to control, regulate, and reduce energy consumption. This document introduces home energy management through dynamic distributed resource management and optimized operation of household appliances in a DSM based simulation platform. The principal purpose of the simulation platform is to illustrate customer-driven DSM operation, and evaluate an estimate for home electricity consumption while minimizing the customer's cost. A heuristic optimization algorithm i.e. Binary Particle Swarm Optimization (BPSO) is used for the optimization of DSM operation in the platform. The platform also simulates the operation of household appliances as a Hybrid Renewable Energy System (HRES). The resource management technique is implemented using an optimization algorithm, i.e. Particle Swarm Optimization (PSO), which determines the distribution of energy obtained from various sources depending on the load. The validity of the platform is illustrated through an example case study for various household scenarios.

Committee: Dr. Lingfeng Wang PhD (Advisor); Dr. Vijay Devabhaktuni PhD (Advisor); Dr. Gursel Serpen PhD (Committee Member) Subjects: Computer Science; Electrical Engineering; Energy; Technology

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

Master of Science in Materials Science and Engineering (MSMSE), Wright State University, 2023, Materials Science and Engineering

The rapid advancement of technology has resulted in a greater need for effective energy storage systems to meet the demands of the transportation and electronics industries. Among various energy storage systems, batteries are the most widely used, primarily because of their ability to store significant amounts of energy. In addition, lithium-ion batteries are prevalent for powering portable electronic devices due to their long cycle life, high energy density, and high operating voltage. The traditional doctor-blade approach has been used over the years for producing batteries. Currently, research is being directed to additively manufacture Li-ion batteries via Drop-on-Demand Inkjet Printing with unique architectures towards further increasing energy density and satisfying special applications. The rheology and dispersion of particles in the slurry are critical parameters that affect the performance and printability of batteries in all production routes. In addition, the quality of lithium-ion batteries, including their electrochemical and durability performance, is significantly impacted by the consistency of the slurries used in their production. Thus, a physics-based model that accurately describes the consistency of these slurries is urgently needed to enable the precise optimization of battery manufacturing processes. This work is to develop a computational model to predict the rheology of electrode ink to be printed via Drop-on-demand inkjet printing. The rheology of electrode ink was modeled based on hydrodynamic and colloidal interactions, which include particle interaction, electrostatic forces, steric repulsive forces, and forces due to adsorbed polymer. MATLAB computer routines were used to solve the equations for forces acting in a different type of colloidal system and, ultimately, to predict the system's viscosity. The results from the computational model developed are validated by comparing them with published experimental results. The model agrees we (open full item for complete abstract)

Committee: Hong Huang Ph.D. (Committee Co-Chair); Ahsan Mian Ph.D. (Committee Co-Chair); Henry D. Young Ph.D. (Committee Member) Subjects: Chemical Engineering; Chemistry; Materials Science; Mechanical Engineering

Doctor of Philosophy, University of Toledo, 2022, Engineering

As the world transitions from fossil to renewable sources of electricity, management of the electrical grid is becoming increasingly complex. Where once generation flowed unidirectionally from large spinning generation to variable loads, in the new world generation is often also stochastic and power flows in multiple directions, including from sources deep within the distribution system. This necessitates changes to the manner in which the grid is controlled. This dissertation explores the possibilities of transactive energy as a means of dis- tributing control throughout this complex system. A transactive energy testbed has been created on the University of Toledo campus which is used to study various con- trol strategies, both transactive and non-transactive, which are useful for integrating distributed energy resources and bringing demand-side assets such as buildings into the control equation. Several experiments with non-transactive control strategies for buildings and DER, which have been performed on this testbed, are discussed. These include experiments with peak-demand management, mitigation of variability from photovoltaic generation using a battery energy storage system, and load following applications. The university is now building a transactive energy network which will allow testing of transactive control systems. Before these experiments take place, it is necessary to understand which use cases which can best be deployed, which will be of the greatest value to participants, and how these can be evaluated for success. The core of this work is a valuation study of transactive energy use cases which develops metrics for operation and evaluation of their performance in both technical and economic terms. Additionally, one transactive supply-side use case is discussed which seeks to build a single transactive actor from the combination of a nuclear power facility with on-site renewable generation and hydrogen electrolysis.

Committee: Raghav Khanna (Advisor); Michael Heben (Committee Member); Ahmad Javaid (Committee Member); Mohammed Niamat (Committee Member); Daniel Georgiev (Committee Member) Subjects: Electrical Engineering

Master of Science, The Ohio State University, 2020, Mechanical Engineering

The increasing penetration of renewable sources of energy has resulted in an increased likelihood of power over-generation and ramp rate requirements at the electricity supplier end. By incorporating temporally varying costs of electricity provided to the customer, the grid supplier may choose to offer demand-response programs that encourage the customer to defer high load activities to periods of low grid load, effectively overcoming these challenges and increasing machine life. Smart homes optimally activate appliances at the appropriate time with an objective to minimize load at high-price periods, so that at the user end, the total electricity price is lowered. The work presented in this thesis focuses first on the development of models for energy demand and generation associated with electric vehicle (EV) charging and solar power generation, and their integration in an existing residential energy modeling framework. For this enhanced residential power demand model, machine learning (ML) techniques are used to develop a prediction of the user activities for single-resident and multi-resident households. The predicted power demand can be integrated into the smart home algorithm to enhance the optimal activation of appliances to minimize electricity cost and inconvenience.

Committee: Stephanie Stockar (Advisor); Manoj Srinivasan (Committee Member) Subjects: Alternative Energy; Artificial Intelligence; Energy; Engineering; Mechanical Engineering

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

Doctor of Philosophy, University of Toledo, 2016, Electrical Engineering

Hybrid power generation system and distributed generation technology are attracting more investments due to the growing demand for energy nowadays and the increasing awareness regarding emissions and their environmental impacts such as global warming and pollution. The price fluctuation of crude oil is an additional reason for the leading oil producing countries to consider renewable resources as an alternative. Saudi Arabia as the top oil exporter country in the word announced the "Saudi Arabia Vision 2030" which is targeting to generate 9.5 GW of electricity from renewable resources. Two of the most promising renewable technologies are wind turbines (WT) and photovoltaic cells (PV). The integration or hybridization of photovoltaics and wind turbines with battery storage leads to higher adequacy and redundancy for both autonomous and grid connected systems. This study presents a method for optimal generation unit planning by installing a proper number of solar cells, wind turbines, and batteries in such a way that the net present value (NPV) is minimized while the overall system redundancy and adequacy is maximized. A new renewable fraction technique (RFT) is used to perform the generation unit planning. RFT was tested and validated with particle swarm optimization and HOMER Pro under the same conditions and environment. Renewable resources and load randomness and uncertainties are considered. Both autonomous and grid-connected system designs were adopted in the optimal generation units planning process. An uncertainty factor was designed and incorporated in both autonomous and grid connected system designs. In the autonomous hybrid system design model, the strategy including an additional amount of operation reserve as a percent of the hourly load was considered to deal with resource uncertainty since the battery storage system is the only backup. While in the grid-connected hybrid system design model, demand response was incorporated to overcome the impact o (open full item for complete abstract)

Committee: Lingfeng Wang (Committee Chair); Hong Wang (Committee Member); Jackson Carvalho (Committee Member); Richard Molyet (Committee Member); Weiqing Sun (Committee Member) Subjects: Electrical Engineering; Energy

Master of Science (M.S.), University of Dayton, 2014, Renewable and Clean Energy

This thesis analyses the potential of large-scale grid-connected solar photovoltaic (PV) and wind power plants in two of Afghanistan's most populous provinces (Balkh and Herat) to meet a fraction of growing electricity demand. The analysis is performed by quantifying resource quality, variability and cost of energy generation. First, the quality of solar and wind resources is quantified by characterizing wind speed and solar radiation and calculating capacity factors and energy yields from hypothetical power plants using measured wind speed and typical solar radiation data. Second, variability of wind and solar resources is quantified by comparing their daily and seasonal profiles with electricity demand profiles, analyzing their impacts on load duration curves and determining their penetration and curtailment levels for various demand scenarios. Finally, cost of energy generated from solar PV and wind power plants is determined. The research shows that future solar PV and wind power plants in Balkh and Herat provinces could achieve very high penetration levels without significant curtailment meaning less reliance on unpredictable and unstable power purchase agreements with neighboring countries, longer life of limited domestic fossil fuel resources such as coal and natural gas, and less imports of diesel fuel with rising costs and unfriendly environmental impacts.

Committee: Robert J. Brecha Ph.D. (Committee Chair); J. Kelly Kissock Ph.D. (Committee Member); Kevin P. Hallinan Ph.D. (Committee Member) Subjects: Electrical Engineering; Energy; Environmental Engineering; Mechanical Engineering

MS, University of Cincinnati, 2014, Engineering and Applied Science: Mechanical Engineering

This thesis describes a novel system that is being developed that utilizes latent thermal energy storage (LTES) to shift residential heating and cooling loads, between 2-4 hr. time periods, away from the electrical power grid (during the utilities' peak demand period) for the main purpose of residential demand-side-management. More and more utilities are now offering residential time-of-day rates (and load interrupt programs) to help improve their load factor as a means to curtail the building of new power generating stations, and will only increase in time with greater implementation and the enabling use of smart meters. TES is ideally suited to capitalize on this fact and stands ready in this proposed new system; running the HVAC equipment during the time of excess system capacity and storing the “hot or cold energy created” in the PCM for later use during the peak system demand period will improve the system's load. This thesis describes the proposed system and the equipment layout along with its operating strategy. In addition to being modular in design and thus allowing for all different size homes, another major key feature of the proposed system is that it is of the “plug-in” type which utilizes the current cooling and heating hardware of the existing home, and as such, is equally applicable to new home construction or retrofits. This thesis also presents the economics of the system and potential benefits to the home owner, more specifically, simple calculations are given showing the estimated monthly operating cost savings when using this TES system with residential time-of-day (TD) rates, over that of the home operating without TES and running on the standard residential service (RS) rate structure. This thesis document provides the detailed mathematical formulation for the solution of planar moving boundary problems using enhanced enthalpy method with given fixed temperature and insulated boundary conditions. The solution methodology and results, obt (open full item for complete abstract)

Committee: Michael Kazmierczak Ph.D. (Committee Chair); Ahmed Elgafy Ph.D. (Committee Member); Frank Gerner Ph.D. (Committee Member) Subjects: Mechanics

Doctor of Philosophy (Ph.D.), Bowling Green State University, 2013, Photochemical Sciences

The solar energy utilization is one of the most promising strategies for catering the ever-increasing energy demand in a renewable manner. For this reason, several approaches are pursued for solar energy storage, one of which involves the photocatalytic splitting of water. Over recent years, much research has been directed towards the design of transition-metal based water oxidation catalysts to obtain oxygen based on transition metal complexes. The major drawback of most of these catalysts is the cost of transition- metal complexes. For these reasons, the main focus of our research is based on the design of a fully organic catalyst suitable for water oxidation. Our group recently discovered that a flavinium ion performs electrode-mediated electrocatalytic water oxidation at large overpotentials. It was found that catalysis occurs only in the presence of the electrodes that produce active oxides on their surfaces. The mechanism of the catalysis by the flavinium ions was proposed to involve the coupling reaction two oxygen-centered radicals, one of which is derived from to the flavin moiety and the other one is formed at the electrode surface. The electrochemical oxidation of the formed peroxide species then proposed to release the oxygen molecule and recover the catalyst. However, it is important to note, that the detailed study of the mechanism is limited due the fact that electrode participates in the catalytic cycle. For these reasons, it is crucial to develop a fully homogeneous system to study the mechanism of the catalysis. One approach towards a fully molecular catalysis involves a system composed of two- iminium ion moieties joined covalently by a suitable linker. The mechanism of a catalysis is proposed to involve four individual steps: (i) pseudobase formation via a reaction of flavinium ions with water; (ii) proton-coupled oxidation of pseudobases to generate alkoxyl radicals; (iii) coupling of alkoxyl radicals to generate the peroxide intermediate; (iv (open full item for complete abstract)

Committee: Ksenija Glusac Ph.D. (Advisor); Thomas Kinstle Ph.D. (Committee Member); Marshall Wilson Ph.D. (Committee Member); Michael Zamkov Ph.D. (Committee Member) Subjects: Chemistry; Energy

Doctor of Philosophy, The Ohio State University, 2012, Industrial and Systems Engineering

The past several years have seen increased interest among governmental bodies, regulators, and energy system planners in renewable energy. Much of this interest has been driven by environmental, energy security, and other concerns surrounding fossil fuels and conventional generation technologies. Integrating renewable resources into existing power grids is challenging and requires thorough analytical studies. Challenges are due to specific characteristics of renewable resources such as uncertainty and variability. These characteristics will affect the way power systems are operated and planned and can cause considerable costs. This dissertation addresses challenges in renewable energy integration from two aspects: short term operations and long term planning. The outcome will help utilities, system operators and energy planners to properly recognize these challenges and develop effective integration policies.

Committee: Ramteen Sioshansi PhD (Advisor); Suvrajeet Sen PhD (Committee Member); William Notz PhD (Committee Member) Subjects: Energy; Engineering; Industrial Engineering; Operations Research

Master of Science (MS), Ohio University, 1990, Electrical Engineering & Computer Science (Engineering and Technology)

Impact of voltage reduction on energy and demand

Committee: Herman Hill (Advisor) Subjects: