Dissertation Defense Announcements

Peak load forecasting is crucial for reliable and effective grid operation. The day-to-day operation of the power grid requires load scheduling and dispatches of different energy resources including Energy Storage System and Demand Side Management programs. An effective implementation of these peak-shaving strategies relies heavily on when the peak demand occurs. Hence, forecasting the timing of peak load is as important as forecasting its magnitude.
A review of relevant literature indicates that there is no inclusive study on the topic of peak timing forecasting. This research aims to bridge the gap between industry requirements and academic research by addressing some key questions. First, the study defines the different forms of peak timing problems that arise in grid operation. Next, we investigate the problem of how we measure the peak timing forecast errors. The research critically reviews error measures used in the literature for peak timing forecasting. Based on the findings five new application-specific error measures are proposed. The research then focuses on one of the manifestations of the peak timing problem, that is, forecasting daily peak hours.
We analyzed the accuracy of peak hour forecasts from a state-of-the-art hourly load forecasting model and set it as the benchmark. The model selection process using different peak timing errors and load shape errors is investigated. Furthermore, two different frameworks for peak hour forecasting have been developed. The effectiveness of the proposed frameworks is empirically demonstrated in two case studies. The first case study is from a medium-sized Utility in the U.S. and the second one is from ISO New England. The proposed models demonstrate improved forecast results on the benchmark model by 12-16% in the test years of the two case studies. Additionally, when the models are only evaluated on the critical days with very high demands, they outperform the benchmark by 25-53%. Findings from this study emphasize the importance of developing explicit models for peak hour forecasting by analyzing the key determinants that vary with geographical location and regional factors.

There is a growing mental health concern among Black Ratchet queer womxn in educational and criminal justice realms and the Covid-19 pandemic has left the educational climate in a state of high stress and anxiety. Consequently, Black Womxn in education are quitting from burnout. According to research, teacher burnout has been a concern for more
than 30 years, but currently there is a crisis. According to research “This is a five-alarm crisis. We are facing an exodus as more than half of our nation’s teachers and other school staff are now indicating they will be leaving education sooner than planned.: (Jotkoff, 2022).
Covid-19 has caused an increase in depression, anxiety, suicide ideation, and other health concerns in this population. External hindrances including racism, sexism, homophobia, and other ideologies rooted in America; negatively influence their mental well-being. Black queer womxn and girls are restricted from obtaining proper access to mental health services that take into consideration how identities are critical factors in mental well-being. This analysis will provide a rationale for utilizing culturally relevant mindfulness practices for Black queer womxn in educational sectors.

Seasonal cold weather and frost action has a major effect on the design, construction, performance, and maintenance of roadways. Frost heaving and thaw weakening are especially problematic, subjecting all elements of a pavement system to significant changes in moisture content, stress, and strain. Nationally, this leads to recurrent annual maintenance costs estimated at over 2 billion dollars, as well as additional economic impacts because of related vehicle damage, road closures, and weight restrictions. Studies identify three basic requirements for frost action; freezing temperatures, availability of water, and frost-susceptible soils. While advances have been made in designing for freezing temperatures and providing for groundwater separation, very little progress has been made in terms of in situ soil improvement. A cost and labor-intensive approach is to replace unsuitable soils. As an alternative, Engineered Water Repellency (EWR), a process in which soils are made hydrophobic is presented as a suitable barrier limiting the transport of water through these soils, resulting in frost heave mitigation. This is achieved by combining soils with cost-effective and environmentally compatible polymers and other complex organic molecules. This process explored through a multi-year project funded by the U.S. National Science foundation, involved laboratory, field, and numerical studies.

Renewable energy resources advancement and offerings are steadily increasing, a major factor leading to its global fast adoption. The connection of these resources to the electric grid, however, needs to be studied to ensure efficiency both from an operational and regulatory standpoint. The IEEE 1547 has been used to establish standards for grid interconnection of some renewable energy resources (RERs). In this dissertation, the operations of RERs connected to the grid with respect to their control, management, and optimization are studied. It is of note that RERs are intermittent in nature and this can have effects on the power quality metrics or utility objectives on either network separately or collectively. For instance, the stability of the grid can be affected due to the low inertia of these resources, which can impact the voltage or the grid frequency. A novel adaptive controller was developed to damp the oscillations caused by these RERs, the controller was initially tested with RERs in one network architecture, and it offers advantages such as dynamically responsive support to the grid to control the frequency, a frequency spectrum was used to determine the amount of support required in an adaptive manner. The architecture was then expanded to a network model that has both transmission and distribution networks integrated together with the interconnection of multiple RERs connected to the grid, the capabilities of the proposed architecture were evaluated with different test cases with different grid events. The architecture had the capability to control multiple generators as well as damp the oscillations observed during the test cases and simulations performed, by adaptively updating the gain of the power system stabilizers (PSS).
On the management side, A new technique was developed with grid-connected RERs that provide real-time visibility of two integrated networks during operation. Presently, the operations don't offer such capabilities as the transmission system operator (TSO) is often times blind to the distribution system operator (DSO). Our technique makes it possible for the transmission network to adjust itself in real-time in case of sudden changes in the distribution network with RERs connected, A stochastic linear optimization technique; Linear decision rule (LDR) that establishes the relationship between the generators in the transmission network and the RERs in the distribution network was implemented, the technique addresses one of the major issues with integrated T&D networks which is boundary mismatch caused by the reverse power flow from the distribution network, in addition to offering the operational advantages required by most utilities like minimization of voltage deviation, and minimization of cost of operation, as it eliminates the need for curtailing RERs which is the current implementation used by most utilities, the technique theorem proof was also discussed. Furthermore, Grid Connected RERs are multiperiod in nature, it is therefore imperative to study their behavior at each time interval, the optimization framework was extended to such studies to handle the reverse power flow operation due to the irradiance daily curve, and the optimal power flow formulation was transformed to multiperiod optimal power flow MPOPF. The effectiveness of the proposed architecture was tested with an irradiance curve, and a typical residential load curve, it demonstrated the capability to reduce the boundary mismatch while ensuring the grid objectives for each network were achieved. Finally, the impact of electric vehicle charging was studied and a management approach was developed, Electric vehicles (EVs) adoption is also increasing impacts of the distribution network on the transmission network with respect to grid penetration, we developed a two-stage stochastic linear optimization in the integrated T&D to handle the uncertainty with electric vehicle charging and compared with effective EV charging management technique that was developed.