Complex modeling and analysis of the energy systems of Afghanistan

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Insinööritieteiden korkeakoulu | Master's thesis
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Date

2021-05-17

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Mcode

Degree programme

Nordic Master Programme in Innovative and Sustainable Energy Engineering (ISEE)

Language

en

Pages

30 + 10

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Abstract

This Master's Thesis aimed to use quantitative analysis to explore different pathways for the sustainable development of Afghanistan. The Open Source Energy Modeling System (OSeMOSYS) was adopted to build an energy model of the country. Electricity demand projections for residential, industrial and commercial sectors were created using both a bottom-up and a top-down approach. These were then used as input data for the optimisation model. Starting from the Reference scenario, three additional scenarios were elaborated: Limit Import scenario, Renewable scenario and National Policies scenario. These showed different options of the least-cost energy mix and explored fundamental aspects to be considered for sustainable development, such as grid access, energy reliability, efficiency and costs, potential of renewable energy. In detail, the Limit Import scenario restricted electricity import up to 60\% by 2050. The Renewable scenario applied the following constraints of minimum RE penetration: 20\% by 2020, 30\% by 2030, 40\% by 2040. The National Policies scenario implemented the capacity of power plants that were already planned and commissioned by the country's future plans. The results highlighted a strong dependency on import as well as a consistent fossil-fuel baseload across all scenarios. Even if the investment costs were decreasing over time, renewables would enter the mix only if strict targets were applied. Hydro power represented the only green technology to play a bigger role in the mix. Overall, the results of this study could be used as an informative source for the national policy makers.

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Supervisor

Järvinen, Mika

Thesis advisor

Sridharan, Vignesh

Keywords

Afghanistan, energy systems, energy demand, OSeMOSYS, scenarios, optimisation

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https://urn.fi/URN:NBN:fi:aalto-202105236920

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