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Operation of electric microgrids under uncertainty

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dc.contributor.authorPanwar, Mayank, author
dc.contributor.authorSuryanarayanan, Siddharth, advisor
dc.contributor.authorChakraborty, Sudipta, committee member
dc.contributor.authorHovsapian, Rob O., committee member
dc.contributor.authorYoung, Peter M., committee member
dc.contributor.authorZimmerle, Daniel J., committee member
dc.date.accessioned2017-06-09T15:40:56Z
dc.date.available2019-06-06T22:59:22Z
dc.date.issued2017
dc.description.abstractOptimization and decision-making are non-trivial in case of multiple, incommensurable, and conflicting objectives. Decision-making becomes more complicated with uncertainty in inputs. Power system operation with electric microgrids subsumes all of the abovementioned aspects. Centralized decision-making in day-ahead dispatch of microgrids with multiple objectives in a grid-connected mode is addressed from the perspective of a power distribution system operator. Uncertainties in the electrical output of variable distributed energy resources and load demand due to forecasting errors are treated statistically by using empirical distributions. Scenarios for simulation are generated using statistics of actual data for solar and load demand forecast. Kantorovich distance measure is used for scenario reduction to maintain computational tractability of the problem. Discrete compromise programming is used for multi-criteria decision-analysis to obtain non-dominated dispatch solutions without generating a computationally expensive Pareto front. Two step look-ahead dynamic program routine is used for dispatch optimization of dispatchable, non-dispatchable solar, and energy storage asset. New performance metrics are developed for reserve management in microgrids using North American Electric Reliability Corporation (NERC) metrics and some previously developed metrics by this researcher. The economic dispatch problem is formulated as a constrained optimization problem with the new metric for reserve as a constraint. Optimization programs are implemented using MATLAB® and power system simulations are performed on standard IEEE 13-node test distribution feeder using the real-time simulation platform—RTDS®. Some potential future developments and applications of performance metrics are presented as future work.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.identifierPanwar_colostate_0053A_14016.pdf
dc.identifier.urihttp://hdl.handle.net/10217/181307
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectdispatch
dc.subjectmicrogrids
dc.subjectuncertainty
dc.subjectmetrics
dc.subjectdecision making
dc.subjectoptimization
dc.titleOperation of electric microgrids under uncertainty
dc.typeText
dcterms.embargo.expires2019-06-06
dcterms.embargo.terms2019-06-06
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineElectrical and Computer Engineering
thesis.degree.grantorColorado State University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)

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