UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

A review of approaches to uncertainty assessment in energy system optimization models

Yue, X; Pye, S; DeCarolis, J; Li, FGN; Rogan, F; Gallachóir, B; (2018) A review of approaches to uncertainty assessment in energy system optimization models. Energy Strategy Reviews , 21 pp. 204-217. 10.1016/j.esr.2018.06.003. Green open access

[img] Text
A Review of Approaches to Uncertainty Assessment in ESOM_Manuscript_Accepted Version.docx - Accepted version

Download (570kB)

Abstract

© 2018 Energy system optimization models (ESOMs) have been used extensively in providing insights to decision makers on issues related to climate and energy policy. However, there is a concern that the uncertainties inherent in the model structures and input parameters are at best underplayed and at worst ignored. Compared to other types of energy models, ESOMs tend to use scenarios to handle uncertainties or treat them as a marginal issue. Without adequately addressing uncertainties, the model insights may be limited, lack robustness, and may mislead decision makers. This paper provides an in-depth review of systematic techniques that address uncertainties for ESOMs. We have identified four prevailing uncertainty approaches that have been applied to ESOM type models: Monte Carlo analysis, stochastic programming, robust optimization, and modelling to generate alternatives. For each method, we review the principles, techniques, and how they are utilized to improve the robustness of the model results to provide extra policy insights. In the end, we provide a critical appraisal on the use of these methods.

Type: Article
Title: A review of approaches to uncertainty assessment in energy system optimization models
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.esr.2018.06.003
Publisher version: https://doi.org/10.1016/j.esr.2018.06.003
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Energy system modelling, Uncertainty, Monte Carlo analysis, Stochastic programming, Robust optimization, Modelling to generate alternatives
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources
URI: https://discovery.ucl.ac.uk/id/eprint/10054384
Downloads since deposit
56Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item