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

Ioannou, A., Angus, A. and Brennan, F. (2017) Stochastic prediction of offshore wind farm LCOE through an integrated cost model. Energy Procedia, 107, pp. 383-389. (doi: 10.1016/j.egypro.2016.12.180)

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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.

Item Type:Articles
Additional Information:This work was supported by grant EP/L016303/1 for Cranfield University, Centre for Doctoral Training in Renewable Energy Marine Structures (REMS) ( from the UK Engineering and Physical Sciences Research Council (EPSRC).
Glasgow Author(s) Enlighten ID:Ioannou, Dr Anastasia
Authors: Ioannou, A., Angus, A., and Brennan, F.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:Energy Procedia
ISSN (Online):1876-6102
Published Online:09 March 2017
Copyright Holders:Copyright © 2017 The Authors
First Published:First published in Energy Procedia 107:383-389
Publisher Policy:Reproduced under a Creative Commons License

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