Planning and Design of Seawater Pumped Hydro Storage Systems (S-PSS) Under Future Climate Change Scenarios Using Machine Learning Techniques in California

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dc.contributorMoftakhari, Hamed Hm
dc.contributorCohen, Sagy Sc
dc.contributor.advisorMoradkhani, Hamid Hm
dc.contributor.authorArslan, Orhan
dc.contributor.otherUniversity of Alabama Tuscaloosa
dc.date.accessioned2021-11-23T14:34:54Z
dc.date.available2021-11-23T14:34:54Z
dc.date.issued2021
dc.descriptionElectronic Thesis or Dissertationen_US
dc.description.abstractClimate change is one of the most critical global issues today due to its widespread impacts on water resources, energy, and agriculture. In order to reduce the emission of greenhouse gases (a major contributor to climate change), California plans to generate 100% of its energy demand from renewable energy sources by 2045. Two major renewable energy sources are solar and wind energy; however, due to differences in the peak hour of energy generation (during afternoon hours) and the energy demand (during late evening), a load balancing system is crucial. Moreover, the future impacts of climate change on energy demand and source are unknown. Therefore, this study aims to plan and design a Seawater Pumped Hydro Storage (S-PSS) to balance curtailments and load balancing. The overarching objectives are (i) comparing five different Global Climate Models (GCMs) from Coupled Model Intercomparison Project-6 (CMIP6) and using the best GCM in predicting the future precipitation and average temperature, (ii) projecting monthly electricity demand and renewable energy supply by 2035, and (iii) developing an ArcToolbox to identify possible S-PSS sites. The oversupply of electricity by the year 2035 was estimated using bias-corrected precipitation and average temperature under the SSP (shared socioeconomic pathway) 245 climate change scenario, using several machine learning algorithms and time series techniques. In order to store this oversupply of electricity, an ArcToolbox was created to locate new S-PSS facilities. The main findings of this study are (a) BCC-CSM2-MR and CanESM5.0.3 CMIP6 GCMs were best suitable for the projection of precipitation and average temperature respectively, (b) Random Forest and autoregressive integrated moving average (ARIMA) methods outperformed other methods in terms of the prediction of demand and supply, respectively, and forecasted 16,231 MWh oversupply, and (c) using the created ArcToolbox, a site for S-PSS was located with a calculated storage capacity of 521 MWh. The detailed quantitative analysis from this study can be useful for both the authorities in California and the grid operators that produce electricity to solve the load-balancing problem arising from the spread of renewable electricity supply.en_US
dc.format.mediumelectronic
dc.format.mimetypeapplication/pdf
dc.identifier.otherhttp://purl.lib.ua.edu/181545
dc.identifier.otheru0015_0000001_0003984
dc.identifier.otherArslan_alatus_0004M_14632
dc.identifier.urihttp://ir.ua.edu/handle/123456789/8216
dc.languageEnglish
dc.language.isoen_US
dc.publisherUniversity of Alabama Libraries
dc.relation.hasversionborn digital
dc.relation.ispartofThe University of Alabama Electronic Theses and Dissertations
dc.relation.ispartofThe University of Alabama Libraries Digital Collections
dc.rightsAll rights reserved by the author unless otherwise indicated.en_US
dc.subjectArcToolbox
dc.subjectBias-correction
dc.subjectCurtailment
dc.subjectMachine learning
dc.subjectRenewables
dc.subjectSeawater Pumped Hydro Power Plants
dc.titlePlanning and Design of Seawater Pumped Hydro Storage Systems (S-PSS) Under Future Climate Change Scenarios Using Machine Learning Techniques in Californiaen_US
dc.typethesis
dc.typetext
etdms.degree.departmentUniversity of Alabama. Department of Civil, Construction, and Environmental Engineering
etdms.degree.disciplineClimate change
etdms.degree.grantorThe University of Alabama
etdms.degree.levelmaster's
etdms.degree.nameM.S.
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