Offshore Renewable Energy : An Exploration of Techno-Economic Feasibility and Reliability through a Computational Optimization Perspective Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/bz60d2079

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  • Offshore renewable energy (ORE) has the potential to be a significant source of future global electricity production, reduce carbon emissions, decrease dependence on energy importation, and stimulate economic growth in coastal and remote areas. The availability and abundance of ORE, paired with growing coastal population centers, position offshore wind, wave, and tidal energy technologies as viable means for providing power to coastal areas. The key to making these technologies feasible is providing electricity through reliable, efficient technology, and at competitive prices. The objective of this research is to explore novel approaches to improving ORE performance, cost, and reliability. In the first two studies, I propose methods to optimize co- located wind-wave installments, investigating the feasibility and potential benefits of placing offshore wind turbines and wave energy converters (WECs) in the same leased ocean area. In the third and fourth studies, I explore the feasibility of emergency wave energy generation during sustained outages in coastal areas. To characterize which machine learning methods are best suited to predict storm-related, sustained transmission outages on the Oregon coast, I compare multiple machine learning regression methods. I then use the results of this study in an analytical cost model for emergency wave energy generation after a sustained outage. The final study reviews reliability-based design optimization applications in offshore renewable energy systems, highlighting areas for future work.
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  • description.provenance : Approved for entry into archive by Margaret Mellinger(margaret.mellinger@oregonstate.edu) on 2017-10-16T23:27:07Z (GMT) No. of bitstreams: 2license_rdf: 1536 bytes, checksum: df76b173e7954a20718100d078b240a8 (MD5)ClarkCaitlynE2017.pdf: 143115837 bytes, checksum: b9e3325cc30a8fd8b3f82f7f82b9ebf0 (MD5)
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  • description.provenance : Submitted by Caitlyn Clark (clarkc@oregonstate.edu) on 2017-09-21T07:45:45ZNo. of bitstreams: 2license_rdf: 1536 bytes, checksum: df76b173e7954a20718100d078b240a8 (MD5)ClarkCaitlynE2017.pdf: 143115837 bytes, checksum: b9e3325cc30a8fd8b3f82f7f82b9ebf0 (MD5)

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