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Smart Energy Management of Multiple Full Cell Powered Applications
Author(s) -
Mohammad Saad Alam
Publication year - 2007
Publication title -
osti oai (u.s. department of energy office of scientific and technical information)
Language(s) - English
Resource type - Reports
DOI - 10.2172/902923
Subject(s) - energy management , schedule , computer science , photovoltaic system , leverage (statistics) , energy storage , hydrogen production , electricity generation , hydrogen fuel , process engineering , automotive engineering , fuel cells , engineering , power (physics) , electrical engineering , hydrogen , energy (signal processing) , statistics , physics , chemistry , mathematics , organic chemistry , quantum mechanics , machine learning , chemical engineering , operating system
In this research project the University of South Alabama research team has been investigating smart energy management and control of multiple fuel cell power sources when subjected to varying demands of electrical and thermal loads together with demands of hydrogen production. This research has focused on finding the optimal schedule of the multiple fuel cell power plants in terms of electric, thermal and hydrogen energy. The optimal schedule is expected to yield the lowest operating cost. Our team is also investigating the possibility of generating hydrogen using photoelectrochemical (PEC) solar cells through finding materials for efficient light harvesting photoanodes. The goal is to develop an efficient and cost effective PEC solar cell system for direct electrolysis of water. In addition, models for hydrogen production, purification, and storage will be developed. The results obtained and the data collected will be then used to develop a smart energy management algorithm whose function is to maximize energy conservation within a managed set of appliances, thereby lowering O/M costs of the Fuel Cell power plant (FCPP), and allowing more hydrogen generation opportunities. The Smart Energy Management and Control (SEMaC) software, developed earlier, controls electrical loads in an individual home to achieve load management objectives such that the total power consumption of a typical residential home remains below the available power generated from a fuel cell. In this project, the research team will leverage the SEMaC algorithm developed earlier to create a neighborhood level control system

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