Chaotic PSO-based VAR control considering renewables using fast probabilistic power flow

Ying Yi Hong, Faa Jeng Lin, Yu Chun Lin, Fu Yuan Hsu

Research output: Contribution to journalArticlepeer-review

32 Scopus citations

Abstract

The roles of reactive power control in a distribution system become essential due to the high penetration of distributed generations (DG) these days. Proper reactive power control can reduce real power losses and regulate the voltage profile in a power system. However, intermittent characteristics of DGs (e.g., renewable energies from wind and solar power) impose uncertainty on power generation in the power system. Therefore, this paper presents a novel fast probabilistic power-flow (FPPF) method based on the Gram-Charlier series expansion to deal with such uncertainty. The FPPF method only deals with stochastic variations of random variables with respect to the expected values, thus reducing the number of iterations. Moreover, the chaotic particle swarm optimization is used to adjust generator voltages, transformer taps, and static compensators to minimize the real power losses while the stochastic voltages satisfy the operational limits. Applicability of the proposed method is verified through simulation using an autonomous 25-bus (Penghu) system and the IEEE 118-bus system. Comparative studies considering traditional probabilistic power-flow methods are performed as well.

Original languageEnglish
Article number6654303
Pages (from-to)1666-1674
Number of pages9
JournalIEEE Transactions on Power Delivery
Volume29
Issue number4
DOIs
StatePublished - Aug 2014

Keywords

  • Particle swarm optimization (PSO)
  • VAR control
  • probabilistic power flow
  • renewable energy

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