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.contributor Moftakhari, Hamed Hm
dc.contributor Cohen, Sagy Sc
dc.contributor.advisor Moradkhani, Hamid Hm Arslan, Orhan 2021-11-23T14:34:54Z 2021-11-23T14:34:54Z 2021
dc.identifier.other u0015_0000001_0003984
dc.identifier.other Arslan_alatus_0004M_14632
dc.description Electronic Thesis or Dissertation
dc.description.abstract Climate 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.
dc.format.medium electronic
dc.format.mimetype application/pdf
dc.language English
dc.language.iso en_US
dc.publisher University of Alabama Libraries
dc.relation.ispartof The University of Alabama Electronic Theses and Dissertations
dc.relation.ispartof The University of Alabama Libraries Digital Collections
dc.relation.hasversion born digital
dc.rights All rights reserved by the author unless otherwise indicated.
dc.subject ArcToolbox en_US
dc.subject Bias-correction en_US
dc.subject Curtailment en_US
dc.subject Machine learning en_US
dc.subject Renewables en_US
dc.subject Seawater Pumped Hydro Power Plants en_US
dc.title Planning and Design of Seawater Pumped Hydro Storage Systems (S-PSS) Under Future Climate Change Scenarios Using Machine Learning Techniques in California en_US
dc.type thesis
dc.type text University of Alabama. Department of Civil, Construction, and Environmental Engineering Climate change The University of Alabama master’s M.S.

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