NDLI: Autonomous Microgrid Design Using Classifier-Guided Sampling

Content Provider	The American Society of Mechanical Engineers (ASME) Digital Collection
Author	Peter, B. Backlund John, P. Eddy
Copyright Year	2015
Abstract	Identifying high-performance, system-level microgrid designs is a significant challenge due to the overwhelming array of possible configurations. Uncertainty relating to loads, utility outages, renewable generation, and fossil generator reliability further complicates this design problem. In this paper, the performance of a candidate microgrid design is assessed by running a discrete event simulation that includes extended, unplanned utility outages during which microgrid performance statistics are computed. Uncertainty is addressed by simulating long operating times and computing average performance over many stochastic outage scenarios. Classifier-guided sampling, a Bayesian classifier-based optimization algorithm for computationally expensive design problems, is used to search and identify configurations that result in reduced average load not served while not exceeding a predetermined microgrid construction cost. The city of Hoboken, NJ, which sustained a severe outage following Hurricane Sandy in October, 2012, is used as an example of a location in which a well-designed microgrid could be of great benefit during an extended, unplanned utility outage. The optimization results illuminate design trends and provide insights into the traits of high-performance configurations.
Sponsorship	Design Engineering Division Computers and Information in Engineering Division
File Format	PDF
ISBN	9780791857076
DOI	10.1115/DETC2015-46107
Volume Number	Volume 2A: 41st Design Automation Conference
Conference Proceedings	ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
Language	English
Publisher Date	2015-08-02
Publisher Place	Boston, Massachusetts, USA
Access Restriction	Subscribed
Subject Keyword	Design Uncertainty Construction Simulation Microgrids Optimization algorithms Statistics as topic Generators Reliability Stress Optimization
Content Type	Text
Resource Type	Article

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