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

Ioannou, Anastasia and Fuzuli, Gulistiani and Brennan, Feargal and Yudha, Satya Widya and Angus, Andrew (2019) Multi-stage stochastic optimization framework for power generation system planning integrating hybrid uncertainty modelling. Energy Economics, 80. pp. 760-776. ISSN 0140-9883

[img]
Preview
Text (Ioannou-etal-EE-2019-Multi-stage-stochastic-optimization-framework-for-power-generation)
Ioannou_etal_EE_2019_Multi_stage_stochastic_optimization_framework_for_power_generation.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo

Download (2MB)| Preview

    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.