Stochastic prediction of offshore wind farm LCOE through an integrated cost model

Anastasia Ioannou, Andrew Angus, Feargal Brennan

Research output: Contribution to journalArticle

16 Citations (Scopus)
18 Downloads (Pure)

Abstract

Common deterministic cost of energy models applied in offshore wind energy installations usually disregard the effect of uncertainty of key input variables - associated with OPEX, CAPEX, energy generation and other financial variables - on the calculation of levelized cost of electricity (LCOE). The present study aims at expanding a deterministic cost of energy model to systematically account for stochastic inputs. To this end, Monte Carlo simulations are performed to derive the joint probability distributions of LCOE, allowing for the estimation of probabilities of exceeding set thresholds of LCOE, determining certain confidence intervals. The results of this study stress the importance of appropriate statistical modelling of stochastic variables in order to reduce modelling uncertainties and contribute to a better informed decision making in renewable energy investments.

Original languageEnglish
Pages (from-to)383-389
Number of pages7
JournalEnergy Procedia
Volume107
DOIs
Publication statusPublished - 1 Feb 2017

Keywords

  • levelised cost of electricity
  • Monte Carlo simulation
  • offshore wind farm
  • probabilistic cost model
  • stochastic inputs

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