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dc.contributor.advisorHoedl, Seth
dc.contributor.advisorGomes, Gabe
dc.contributor.authorChen, Liwei
dc.date.accessioned2019-09-10T23:30:49Z
dc.date.available2019-09-10T23:30:49Z
dc.date.issued2019-04
dc.identifier.urihttp://hdl.handle.net/10945/63203
dc.descriptionNPS CIVINS (Civilian Institutions) Thesis documenten_US
dc.descriptionApproved for public release; distribution is unlimiteden_US
dc.descriptionCapstone Projecten_US
dc.description.abstractRural communities are facing pressure to increase broadband connectivity for their residents to maintain economic growth, however, due to their low populations, broadband projects are typically not financially feasible with current investment models. The Post Road Foundation project team evaluated the financial feasibility of distributed wind and solar energy resources for Presque Isle Electric and Gas (PIEG), a utility cooperative in Northern Michigan. The team identified that solar installations for Commercial and Industrial customers in the 250kW range could be financially viable due to increased array sizes and rate structure incentives. In order to further test the feasibility of solar and battery arrays for larger utility customers, the team built a long short term recurrent neural network to aid in prediction of future load profile. This load profile was used to optimize solar array and battery size to minimize capital investment costs and reduce payback periods.en_US
dc.description.sponsorshipNPS CIVINSen_US
dc.format.extent23 p.en_US
dc.publisherMonterey, California. Naval Postgraduate Schoolen_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. As such, it is in the public domain, and under the provisions of Title 17, United States Code, Section 105, may not be copyrighted.en_US
dc.titleSmart utility systems for rural electric cooperativesen_US
dc.typeThesisen_US
dc.subject.authorDistributed Energy Resources, Synthetic Load Profile, Recurrent Neural Network, Optimization, Presque Isle Gas and Electric, Post Road Foundationen_US
dc.description.serviceLCDRen_US
etd.thesisdegree.levelMastersen_US


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