A self-learning teaching-learning based optimization for dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads

Zhile Yang, Kang Li, Qun Niu, Yusheng Xue, Aoife Foley

Research output: Contribution to journalArticle

64 Citations (Scopus)
290 Downloads (Pure)

Abstract

Economic and environmental load dispatch aims to determine the amount of electricity generated from power plants to meet load demand while minimizing fossil fuel costs and air pollution emissions subject to operational and licensing requirements. These two scheduling problems are commonly formulated with non-smooth cost functions respectively considering various effects and constraints, such as the valve point effect, power balance and ramp rate limits. The expected increase in plug-in electric vehicles is likely to see a significant impact on the power system due to high charging power consumption and significant uncertainty in charging times. In this paper, multiple electric vehicle charging profiles are comparatively integrated into a 24-hour load demand in an economic and environment dispatch model. Self-learning teaching-learning based optimization (TLBO) is employed to solve the non-convex non-linear dispatch problems. Numerical results on well-known benchmark functions, as well as test systems with different scales of generation units show the significance of the new scheduling method.
Original languageEnglish
Pages (from-to)298-307
Number of pages10
JournalJournal of Modern Power Systems and Clean Energy
Volume2
Issue number4
Early online date16 Dec 2014
DOIs
Publication statusPublished - Dec 2014

Keywords

  • Economic dispatch, Environmental dispatch, Plug-in electric vehicle , Self-learning, Teaching learning based optimization, Peak charging, Off-peak charging, Stochastic charging

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