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

Irawan, Chandra Ade, Hofman, Peter S., Chan, Hing Kai, and Paulraj, Antony (2022) A stochastic programming model for an energy planning problem: formulation, solution method and application. Annals of Operations Research, 311 (2). pp. 695-730.

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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.

Item ID: 75979
Item Type: Article (Research - C1)
ISSN: 1572-9338
Keywords: Energy planning, Stochastic programming, Multi-objective optimization
Copyright Information: © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021
Date Deposited: 12 Sep 2022 23:53
FoR Codes: 35 COMMERCE, MANAGEMENT, TOURISM AND SERVICES > 3507 Strategy, management and organisational behaviour > 350712 Production and operations management @ 100%
SEO Codes: 15 ECONOMIC FRAMEWORK > 1503 Management and productivity > 150302 Management @ 75%
17 ENERGY > 1703 Energy storage, distribution and supply > 170305 Energy systems and analysis @ 25%
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