A stochastic programming model for an energy planning problem: formulation, solution method and application

Chandra Ade Irawan, Peter S. Hofman, Hing Kai Chan, Antony Paulraj

Research output: Journal PublicationArticlepeer-review

5 Citations (Scopus)
109 Downloads (Pure)


The paper investigates national/regional power generation expansion planning for medium/long-term analysis in the presence of electricity demand uncertainty. A two-stage stochastic programming is designed to determine the optimal mix of energy supply sources with the aim to minimise the expected total cost of electricity generation considering the total carbon dioxide emissions produced by the power plants. Compared to models available in the extant literature, the proposed stochastic generation expansion model is constructed based on sets of feasible slots (schedules) of existing and potential power plants. To reduce the total emissions produced, two approaches are applied where the first one is performed by introducing emission costs to penalise the total emissions produced. The second approach transforms the stochastic model into a multi-objective problem using the ϵ-constraint method for producing the Pareto optimal solutions. As the proposed stochastic energy problem is challenging to solve, a technique that decomposes the problem into a set of smaller problems is designed to obtain good solutions within an acceptable computational time. The practical use of the proposed model has been assessed through application to the regional power system in Indonesia. The computational experiments show that the proposed methodology runs well and the results of the model may also be used to provide directions/guidance for Indonesian government on which power plants/technologies are most feasible to be built in the future.

Original languageEnglish
Pages (from-to)695-730
Number of pages36
JournalAnnals of Operations Research
Issue number2
Early online date4 Jan 2021
Publication statusPublished Online - 4 Jan 2021


  • Energy planning
  • Multi-objective optimization
  • Stochastic programming

ASJC Scopus subject areas

  • Decision Sciences (all)
  • Management Science and Operations Research


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