Date of Award
8-22-2025
Date Published
September 2025
Degree Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Mechanical and Aerospace Engineering
Advisor(s)
Bing Dong
Subject Categories
Engineering | Mechanical Engineering
Abstract
With the development of the power grid, the world's industries and economies have experienced a significant boost over the past centuries. However, as the demand for electricity from the grid continues to grow, utility markets face increasingly challenging tasks in balancing supply and demand. Additionally, integrating renewable energy sources such as solar, hydroelectric, and wind power into the grid adds further complexities to maintaining grid stability. In the United States, buildings account for about 76% of electricity consumption and 40% of greenhouse gas emissions, making them a major contributor to peak demand, which is nearly 80%. Therefore, buildings play a key role in enabling energy flexibility within the grid. Today, with the adoption of distributed energy resources (DERs) like photovoltaic (PV) systems, combined heat and power (CHP) units, and electric vehicles (EVs), buildings have become even better equipped to enhance energy flexibility. Notably, EVs have gained recognition for their ability to provide benefits to grid flexibility as their market share increases. However, due to a lack of data, there is a shortage of empirical evidence characterizing how EVs will impact energy flexibility. Also, there is no effective solution to better control the EV operations to fully unlock the potential of EVs to achieve energy flexibility. To fill in these gaps, this study examines the effects of EV charging and discharging behaviors by conducting a comprehensive analysis and optimization on a smart meter dataset, then expanding to an urban-scale simulation study through a data-driven approach for load data generation. It hypothesizes that an advanced hieratical control framework designs a personalized pricing structure, taking into account of diverse occupancy behaviors, can optimally regulate EV charging and discharging behaviors, while simultaneously providing grid services as peak load reduction, increasing grid profitability, reducing overall CO_2 emission, and lowering utility costs for users. Through data processing to understand diverse EV charging behaviors and occupancy schedules, this study developed a hierarchical control framework incorporating mutual optimization of EV charging/discharging scheduling and dynamic pricing. This empirical approach quantifies EV potential for enhancing residential grid flexibility while considering economic, environmental, and grid stability impacts. The study analyzed a smart meter dataset of 225 residential EV customers from Salt River Project (SRP) in Phoenix, Arizona. This community-scale analysis provided proof-of-concept validation before scaling up using a Conditional Generative Adversarial Network (cGAN) to generate realistic building load profiles replicating measured data distributions for urban-scale simulation. The same control framework and data processing procedure follows the same as community-scale study. Community-scale results demonstrated the framework's effectiveness across various control objectives, achieving daily averages of: 22.3% peak load reduction, 2.1% decrease in user electricity bills, 21.3% reduction in utility generation costs, 2.0% grid profitability increase, and 1.7 metric tons CO2 emission reductions. Urban-scale simulation revealed enhanced performance with 28.1% peak load reduction, 0.7% user bill reduction, 6.3% utility generation cost reduction, 1.3% grid profitability increase, and 1.3 metric tons CO2 reduction. This research contributes to understanding: 1) empirical evidence of EV flexibility potential from comprehensive smart meter data; 2) smart charging/discharging strategies incorporating user behavior insights; 3) innovative pricing schemas enabling energy flexibility while accounting for EVs' unique grid role; and 4) scaling findings from measured data to urban-scale simulations exploring EV impact on grid stress relief.
Access
Open Access
Recommended Citation
Li, Yuewei, "A HIERATICAL CONTROL FRAMEWORK FOR ASSESSING EV’S IMPACT ON RESIDENTIAL ENERGY FLEXIBILITY" (2025). Dissertations - ALL. 2202.
https://surface.syr.edu/etd/2202
