Multi-stage stochastic optimization framework for power generation system planning integrating hybrid uncertainty modelling

Anastasia Ioannou, Gulistiani Fuzuli, Feargal Brennan, Satya Widya Yudha, Andrew Angus

Research output: Contribution to journalArticle

11 Citations (Scopus)
63 Downloads (Pure)

Abstract

In this paper, a multi-stage stochastic optimization (MSO) method is proposed for determining the medium to long term power generation mix under uncertain energy demand, fuel prices (coal, natural gas and oil) and, capital cost of renewable energy technologies. The uncertainty of future demand and capital cost reduction is modelled by means of a scenario tree configuration, whereas the uncertainty of fuel prices is approached through Monte Carlo simulation. Global environmental concerns have rendered essential not only the satisfaction of the energy demand at the least cost but also the mitigation of the environmental impact of the power generation system. As such, renewable energy penetration, CO 2,eq mitigation targets, and fuel diversity are imposed through a set of constraints to align the power generation mix in accordance to the sustainability targets. The model is, then, applied to the Indonesian power generation system context and results are derived for three cases: Least Cost option, Policy Compliance option and Green Energy Policy option. The resulting optimum power generation mixes, discounted total cost, carbon emissions and renewable share are discussed for the planning horizon between 2016 and 2030.

Original languageEnglish
Pages (from-to)760-776
Number of pages17
JournalEnergy Economics
Volume80
Early online date3 Mar 2019
DOIs
Publication statusPublished - 1 May 2019

Keywords

  • hybrid uncertainty modelling
  • Indonesia
  • Monte Carlo simulation
  • multi-stage stochastic optimization
  • power generation planning
  • scenario tree

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