Three papers on inverse optimization algorithms, PEV sales forecasting, and PEVs' potential impact on power systems

dc.contributor.advisor Lizhi Wang
dc.contributor.author Duan, Zhaoyang
dc.contributor.department Industrial and Manufacturing Systems Engineering
dc.date 2018-08-11T17:02:28.000
dc.date.accessioned 2020-06-30T02:51:16Z
dc.date.available 2020-06-30T02:51:16Z
dc.date.copyright Wed Jan 01 00:00:00 UTC 2014
dc.date.embargo 2001-01-01
dc.date.issued 2014-01-01
dc.description.abstract <p>This thesis consists of three journal papers that I have been worked on during my PhD program of study.</p> <p>The first paper presents heuristic algorithms that are designed to be implemented and executed in parallel with an existing algorithm in order to overcome its two limitations. Computational experiments show that implementing the heuristic algorithm on one auxiliary processor in parallel with the existing algorithm on the main processor significantly improves its computational efficiency, in addition to providing a series of improving feasible upper bound solutions.</p> <p>In the second paper, we present two interactive models to jointly forecast PEV sales and the diurnal recharging load curve in the U.S. between 2012 and 2020. A case study is conducted for the Midwest ISO region. Compared to the sales forecasts from the literature, our results turn out to be less optimistic. Our recharging load forecast results also suggest that, if appropriately managed, the impact of PEVs on electricity load would not be overwhelming in the next decade.</p> <p>The third paper focuses on assessing and mitigating the potential impact of PEVs recharging load on power systems, Case study show that electricity rates with higher flexibility induces PEVs to have less impact on the power systems in terms of both generation cost and uncertainties. Also, PEV users higher recharging behavior responsiveness will improve the effectiveness of price incentives and the control ability of the power utilities under all types of TOU rates mechanisms.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/etd/13660/
dc.identifier.articleid 4667
dc.identifier.contextkey 5777348
dc.identifier.doi https://doi.org/10.31274/etd-180810-995
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath etd/13660
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/27847
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/etd/13660/Duan_iastate_0097E_14062.pdf|||Fri Jan 14 19:57:59 UTC 2022
dc.subject.disciplines Industrial Engineering
dc.subject.disciplines Operational Research
dc.subject.keywords Inverse optimization
dc.subject.keywords optimize electricity rate
dc.subject.keywords Parallel computing
dc.subject.keywords PEV sales forecasting
dc.subject.keywords Risk measures
dc.title Three papers on inverse optimization algorithms, PEV sales forecasting, and PEVs' potential impact on power systems
dc.type article
dc.type.genre dissertation
dspace.entity.type Publication
relation.isOrgUnitOfPublication 51d8b1a0-5b93-4ee8-990a-a0e04d3501b1
thesis.degree.level dissertation
thesis.degree.name Doctor of Philosophy
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