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Andrew Curtis Elmore
Researcher at Missouri University of Science and Technology
Publications - 53
Citations - 642
Andrew Curtis Elmore is an academic researcher from Missouri University of Science and Technology. The author has contributed to research in topics: Wind power & Renewable energy. The author has an hindex of 11, co-authored 53 publications receiving 563 citations. Previous affiliations of Andrew Curtis Elmore include URS Corporation & University of Missouri.
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Optimal Sizing of a Vanadium Redox Battery System for Microgrid Systems
TL;DR: In this article, an analytical method to determine the optimal ratings of vanadium redox battery energy storage based on an optimal scheduling analysis and cost-benefit analysis for microgrid applications is presented.
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A Field Validated Model of a Vanadium Redox Flow Battery for Microgrids
TL;DR: A reduced order circuit model of the vanadium redox flow battery is developed and its experimental performance efficiency during deployment is analyzed to address the implementation issues of the VRB application in a photovoltaic-based microgrid system.
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Performance Characterization for Photovoltaic-Vanadium Redox Battery Microgrid Systems
TL;DR: In this paper, a performance characterization analysis of a PV-VRB microgrid system for military installations under different conditions of load and weather was performed at Fort Leonard Wood in Missouri, USA.
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Performance evaluation of energy efficient lighting associated with renewable energy applications
TL;DR: In this article, the authors compared two LED products designed for exterior lighting to traditional metal halide lamps, and quantified the illuminance provided by each lighting system, diesel consumption rates associated with powering the lights and/or charging the batteries and UCAPs.
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Performance Prediction of a Vanadium Redox Battery for Use in Portable, Scalable Microgrids
TL;DR: A model is created to determine system performance based on known climatic and load data collected and analyzed over an extended time period and allows for appropriate sizing of the PV array and discretionary loads based on required energy density of the system.