An optimal and distributed demand response strategy with electric vehicles in the smart grid

Zhao Tan, Peng Yang, Arye Nehorai

Research output: Contribution to journalArticlepeer-review

245 Scopus citations

Abstract

In this paper, we propose a new model of demand response management for the future smart grid that integrates plug-in electric vehicles and renewable distributed generators. A price scheme considering fluctuation cost is developed. We consider a market where users have the flexibility to sell back the energy generated from their distributed generators or the energy stored in their plug-in electric vehicles. A distributed optimization algorithm based on the alternating direction method of multipliers is developed to solve the optimization problem, in which consumers need to report their aggregated loads only to the utility company, thus ensuring their privacy. Consumers can update their loads scheduling simultaneously and locally to speed up the optimization computing. Using numerical examples, we show that the demand curve is flattened after the optimization, even though there are uncertainties in the model, thus reducing the cost paid by the utility company. The distributed algorithms are also shown to reduce the users' daily bills.

Original languageEnglish
Article number6728731
Pages (from-to)861-869
Number of pages9
JournalIEEE Transactions on Smart Grid
Volume5
Issue number2
DOIs
StatePublished - Mar 2014

Keywords

  • Alternating direction method of multipliers
  • demand response
  • distributed optimization
  • electric vehicle
  • fluctuation cost
  • smart grid

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