ADMM-Based Multiperiod Optimal Power Flow Considering Plug-In Electric Vehicles Charging

Hua Fan, Chao Duan, Chuan Ke Zhang, Lin Jiang, Chengxiong Mao, Dan Wang*

*Corresponding author for this work

Research output: Contribution to journalArticle

14 Scopus citations

Abstract

When plug-in electric vehicles (PEVs) participate in grid operation, the intertemporal feature of PEVs charging transforms the traditional optimal power flow (OPF) problem into multiperiod OPF (MOPF) problem. In the case that the population of PEVs is huge, the large number of variables and constraints renders the centralized solution technique unsuitable to solve the MOPF problem. Therefore, a distributed algorithm based on alternating direction method of multipliers is developed to decompose the MOPF into two update steps that are solved in an alternating and iterative style. To improve the solution efficiency, the second update step is transformed into a Euclidean projection problem by approximating the original objective with a surrogate function. Then, a projection algorithm is utilized to solve the approximate problem. Numerical results show that this reformulated model obtains suboptimal solutions with small relative error, but gains considerable speed-up. Furthermore, its scalability and effectiveness are tested in the 119-bus and 906-bus distribution networks.

Original languageEnglish (US)
Pages (from-to)3886-3897
Number of pages12
JournalIEEE Transactions on Power Systems
Volume33
Issue number4
DOIs
StatePublished - Jul 2018

Keywords

  • Plug-in electric vehicles
  • alternating direction method of multipliers (ADMM)
  • multiperiod optimal power flow
  • projection algorithm

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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