Current Search: Colucci, Raymond A. (x)
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Title
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Location management in mobile networks.
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Creator
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Colucci, Raymond A., Florida Atlantic University, Mahgoub, Imad
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Abstract/Description
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Location management in a mobile network provides functions to locate, identify, and validate a terminal or user. The purpose of this thesis is to describe a scheme that would be useful in a wireless network for managing the location of mobile users. This thesis presents a new, distributed location management strategy for mobile systems. Its features are fast location update and query, load balancing among location servers, and scalability. The strategy employs dynamic hashing techniques and...
Show moreLocation management in a mobile network provides functions to locate, identify, and validate a terminal or user. The purpose of this thesis is to describe a scheme that would be useful in a wireless network for managing the location of mobile users. This thesis presents a new, distributed location management strategy for mobile systems. Its features are fast location update and query, load balancing among location servers, and scalability. The strategy employs dynamic hashing techniques and quorums to manage location update and query operations. Location information of a mobile host is replicated at a subset of location servers.
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Date Issued
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2000
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PURL
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http://purl.flvc.org/fcla/dt/12712
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Subject Headings
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Mobile communication systems, Mobile computing
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Format
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Document (PDF)
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Title
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OPTIMIZATION OF BATTERY OPERATION USING ARTIFICIAL INTELLIGENCE TO MINIMIZE THE ELECTRICITY COST IN A MICROGRID WITH RENEWABLE ENERGY SOURCES AND ELECTRIC VEHICLES.
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Creator
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Colucci, Raymond A., Mahgoub, Imadeldin, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
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Abstract/Description
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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,...
Show moreThe 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.
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Date Issued
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2024
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PURL
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http://purl.flvc.org/fau/fd/FA00014502
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Subject Headings
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Electric vehicles, Electric vehicles--Batteries, Renewable energy, Artificial intelligence
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Format
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Document (PDF)
