Off-campus UNL users: To download campus access dissertations, please use the following link to log into our proxy server with your NU ID and password. When you are done browsing please remember to return to this page and log out.
Non-UNL users: Please talk to your librarian about requesting this dissertation through interlibrary loan.
Data-Driven Framework for Predicting and Scheduling Household Charging of EVS
The increasing prevalence of EV charging poses challenges for power grid stability and quality due to high charging load demands. Without effective energy management strategies for EV charging, the simultaneous power demand from numerous EVs can strain the electric grid, impacting power quality and the wholesale electricity market. To address these challenges, this dissertation presents a comprehensive framework comprising five critical tasks: analyzing EV charging behavior, optimizing charging schedules, developing predictive models, analyzing aggregated impacts, and evaluating implications of predicted user behavior on scheduling. By examining EV charging behavior at household and public charging stations, this study aims to understand patterns and variations in charging sessions. The framework introduces a centralized scheduling approach for household charging stations to reduce peak demand and costs, relying on accurate knowledge of EV charging behavior. Machine learning and linear regression models are utilized to predict session charging parameters, with Random Forest models outperforming other methods, yet uncertainties persist in the predictions. The study also investigates aggregate demand and connectivity of multiple EV users, revealing the potential to predict aggregate trends by incorporating session predictions. Evaluating the day-ahead scheduling framework implemented with predicted charging data using actual data highlights challenges in meeting user demand due to prediction errors. This research provides valuable insights into EV charging behavior, emphasizing the significance of accurate data for strategic scheduling. While machine learning models show potential in predicting EV charging behavior, limited correlation between session variables and available information at plugin is observed. Challenges arise from errors in session predictions when scheduling EV charging for a group of users, suggesting the need for a more decentralized approach.
Architectural engineering|Electrical engineering|Automotive engineering
Almaghrebi, Ahmad, "Data-Driven Framework for Predicting and Scheduling Household Charging of EVS" (2023). ETD collection for University of Nebraska - Lincoln. AAI30575591.