ATTENTION: The works hosted here are being migrated to a new repository that will consolidate resources, improve discoverability, and better show UTA's research impact on the global community. We will update authors as the migration progresses. Please see MavMatrix for more information.
Show simple item record
dc.contributor.advisor | Rosenberger, Jay M. | |
dc.contributor.advisor | Chen, Victoria | |
dc.creator | Fallahi, Alireza | |
dc.date.accessioned | 2020-08-03T17:46:15Z | |
dc.date.available | 2020-08-03T17:46:15Z | |
dc.date.created | 2019-08 | |
dc.date.issued | 2019-08-29 | |
dc.date.submitted | August 2019 | |
dc.identifier.uri | http://hdl.handle.net/10106/29294 | |
dc.description.abstract | This research describes a real-time optimization model for multi-agent demand response (DR) from a Load Serving Entity (LSE) perspective. We formulate two infinite horizon stochastic optimization models; specifically, an LSE model and a dynamic pricing customer model. The objective of these models is to minimize long-term cost and discomfort penalty of the LSE and dynamic pricing customers. We solve a deterministic finite horizon linear program as an approximation of the suggested stochastic model and provide computational experiments. In stochastic programming (SP), a wait-and-see solution is at least as good as an optimal policy. On the other hand, a policy that uses the expected value problem is never as good as an optimal policy. This is well established in SP when there is a single agent. A question arises whether bounds exist when we have two agents. The present study develops a research methodology to answer this question. Our experiments show that if we have two separate agents, and both agents get perfect information, this can be worse compared to both agents doing the mean value problem. Nevertheless, we have found that there are bounds when the first stage follows the same set of actions. A two-agent demand response problem has been used as a case study to show this claim. | |
dc.format.mimetype | application/pdf | |
dc.language.iso | en_US | |
dc.subject | Linear programming | |
dc.subject | Multi-agent demand response | |
dc.subject | Demand side management | |
dc.subject | Dynamic pricing customers | |
dc.subject | Stochastic bounds | |
dc.subject | Smart grid | |
dc.title | A MULTI-AGENT DEMAND RESPONSE PLANNING AND OPERATIONAL OPTIMIZATION FRAMEWORK | |
dc.type | Thesis | |
dc.degree.department | Industrial and Manufacturing Systems Engineering | |
dc.degree.name | Doctor of Philosophy in Industrial Engineering | |
dc.date.updated | 2020-08-03T17:46:16Z | |
thesis.degree.department | Industrial and Manufacturing Systems Engineering | |
thesis.degree.grantor | The University of Texas at Arlington | |
thesis.degree.level | Doctoral | |
thesis.degree.name | Doctor of Philosophy in Industrial Engineering | |
dc.type.material | text | |
dc.creator.orcid | 0000-0002-3744-4380 | |
local.embargo.terms | 2021-08-01 | |
local.embargo.lift | 2021-08-01 | |
Files in this item
- Name:
- FALLAHI-DISSERTATION-2019.pdf
- Size:
- 1.761Mb
- Format:
- PDF
This item appears in the following Collection(s)
Show simple item record