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dc.contributor.authorZhou, Datong P.
dc.contributor.authorRoozbehani, Mardavij
dc.contributor.authorDahleh, Munther A
dc.contributor.authorTomlin, Claire J.
dc.date.accessioned2020-12-23T16:49:42Z
dc.date.available2020-12-23T16:49:42Z
dc.date.issued2018-01
dc.date.submitted2017-12
dc.identifier.isbn9781509028733
dc.identifier.urihttps://hdl.handle.net/1721.1/128910
dc.description.abstractThis paper analyzes the impact of peer effects on electricity consumption of a network of rational, utility-maximizing users. Users derive utility from consuming electricity as well as consuming less energy than their neighbors. However, a disutility is incurred for consuming more than their neighbors. To maximize the profit of the load-serving entity that provides electricity to such users, we develop a two-stage game-theoretic model, where the entity sets the prices in the first stage. In the second stage, consumers decide on their demand in response to the observed price set in the first stage so as to maximize their utility. To this end, we derive theoretical statements under which such peer effects reduce aggregate user consumption. Further, we obtain expressions for the resulting electricity consumption and profit of the load serving entity for the case of perfect price discrimination and a single price under complete information, and approximations under incomplete information. Simulations suggest that exposing only a selected subset of all users to peer effects maximizes the entity's profit.en_US
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/cdc.2017.8264229en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcearXiven_US
dc.titleHow peer effects influence energy consumptionen_US
dc.typeArticleen_US
dc.identifier.citationZhou, Datong P. et al. "How peer effects influence energy consumption." IEEE 56th Annual Conference on Decision and Control (CDC), December 2017, Melbourne, Australia, Institute of Electrical and Electronics Engineers, January 2018. © 2017 IEEEen_US
dc.contributor.departmentMassachusetts Institute of Technology. Laboratory for Information and Decision Systemsen_US
dc.relation.journalIEEE 56th Annual Conference on Decision and Control (CDC)en_US
dc.eprint.versionOriginal manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2019-05-14T16:47:58Z
dspace.date.submission2019-05-14T16:47:58Z
mit.metadata.statusComplete


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