Date of Award
2024
Document Type
Dissertation
Publication Status
Version of Record
Submission Date
October 2026
Thesis/Dissertation Advisor [Chair]
Mahgoub, Imadeldin
Abstract
The increasing integration of renewable energy sources (RES) and electric vehicles (EVs) into microgrids presents both opportunities and challenges in terms of optimizing energy use and minimizing electricity costs. This dissertation explores the development of an advanced optimization framework using artificial intelligence (AI) to enhance battery operation in microgrids. The proposed solution leverages AI techniques to dynamically manage the charging and discharging of batteries, considering fluctuating energy demands, variable electricity pricing, and intermittent RES generation. By employing a fuzzy logic-based control algorithm, the system intelligently allocates energy from solar power, grid electricity, and battery storage, while coordinating EV charging schedules to reduce peak demand charges. The optimization framework integrates predictive modeling for energy consumption and generation, alongside real-time data from weather forecasts and electricity markets, to make informed decisions. Additionally, the approach considers the trade-off between maximizing renewable energy usage and minimizing reliance on costly grid power during peak hours.
Extent
187 p.
Recommended Citation
Colucci, Raymond A., "OPTIMIZATION OF BATTERY OPERATION USING ARTIFICIAL INTELLIGENCE TO MINIMIZE THE ELECTRICITY COST IN A MICROGRID WITH RENEWABLE ENERGY SOURCES AND ELECTRIC VEHICLES" (2024). Electronic Theses and Dissertations 2020 - Present. 1468.
https://digitalcommons.fau.edu/etd_general/1468
Identifier
FA00014502
Comments
Includes bibliography.|Dissertation (PhD)--Florida Atlantic University, 2024.|FAU Electronic Theses and Dissertations Collection|Includes bibliography.