Charging and discharging control of plug-in electric vehicles with uncertainties via robust model predictive control method

Peng Wang, Long Ran, Yunfeng Shao, Suli Zou, Zhongjing Ma

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

With the development of the Plug-in Electric Vehicles (PEVs), it has become a significant part of electric load. The optimal PEVs' charging and discharging scheduling is a vital problem when PEVs connect into the smart grid. In most literatures, the charging and discharging procedure is assumed idealized. In reality, many uncertainties are exist during the procedure, such as the conversion efficiency, temperature and other aspects, but they are not easy to be expressed as a fixed term. In this paper, we use a random but bounded uncertainties to describe the uncertainties. Meanwhile, based on robust model predictive control (RMPC) method, we introduce the disturbance invariant set to solve the PEVs scheduling problem with uncertainties and design a feedback control law to guarantee the feasibility of it. A distributed method to reduce the computation complexity of the uncertain problem. At last, some simulations demonstrate the feasibility of the proposed centralized and distributed methods.

Original languageEnglish
Title of host publicationProceedings of the 36th Chinese Control Conference, CCC 2017
EditorsTao Liu, Qianchuan Zhao
PublisherIEEE Computer Society
Pages2644-2650
Number of pages7
ISBN (Electronic)9789881563934
DOIs
Publication statusPublished - 7 Sept 2017
Event36th Chinese Control Conference, CCC 2017 - Dalian, China
Duration: 26 Jul 201728 Jul 2017

Publication series

NameChinese Control Conference, CCC
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927

Conference

Conference36th Chinese Control Conference, CCC 2017
Country/TerritoryChina
CityDalian
Period26/07/1728/07/17

Keywords

  • PEV
  • RMPC
  • disturbance invariant set
  • uncertainty

Fingerprint

Dive into the research topics of 'Charging and discharging control of plug-in electric vehicles with uncertainties via robust model predictive control method'. Together they form a unique fingerprint.

Cite this

Wang, P., Ran, L., Shao, Y., Zou, S., & Ma, Z. (2017). Charging and discharging control of plug-in electric vehicles with uncertainties via robust model predictive control method. In T. Liu, & Q. Zhao (Eds.), Proceedings of the 36th Chinese Control Conference, CCC 2017 (pp. 2644-2650). Article 8027762 (Chinese Control Conference, CCC). IEEE Computer Society. https://doi.org/10.23919/ChiCC.2017.8027762