A probabilistic method for calculating the usefulness of a store with finite energy capacity for smoothing electricity generation from wind and solar power

D.G. Infield, John P Barton

Research output: Contribution to journalArticle

55 Citations (Scopus)

Abstract

This paper describes a novel method of modelling an energy store used to match the power output from a wind turbine and a solar PV array to a varying electrical load. The model estimates the fraction of time that an energy store spends full or empty. It can also estimate the power curtailed when the store is full and the unsatisfied demand when the store is empty. The new modelling method has been validated against time–stepping methods and shows generally good agreement over a wide range of store power ratings, store efficiencies, wind turbine capacities and solar PV capacities.

Example results are presented for a system with 1 MW of wind power capacity, 2 MW of photovoltaic capacity, an energy store of 75% efficiency and a range of loads from 0 to 3 MW average.
LanguageEnglish
Pages943-948
Number of pages5
JournalJournal of Power Sources
Volume162
Issue number2
Early online date11 Aug 2005
DOIs
Publication statusPublished - 22 Nov 2006

Fingerprint

electricity
smoothing
Wind turbines
Solar energy
Wind power
Electricity
wind turbines
solar arrays
energy
ratings
estimates
output

Keywords

  • energy storage
  • stand-alone power systems
  • modelling
  • photovoltaics
  • voltage control
  • wind power generation

Cite this

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AB - This paper describes a novel method of modelling an energy store used to match the power output from a wind turbine and a solar PV array to a varying electrical load. The model estimates the fraction of time that an energy store spends full or empty. It can also estimate the power curtailed when the store is full and the unsatisfied demand when the store is empty. The new modelling method has been validated against time–stepping methods and shows generally good agreement over a wide range of store power ratings, store efficiencies, wind turbine capacities and solar PV capacities. Example results are presented for a system with 1 MW of wind power capacity, 2 MW of photovoltaic capacity, an energy store of 75% efficiency and a range of loads from 0 to 3 MW average.

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