Integrated spatio-temporal techno-economic approach for modeling multi-sectoral bioenergy deployment

Abstract

Although aspects of long-term planning are commonly taken into account in current analyses of bioenergy policy scenarios, spatial representations of the bioenergy supply chain are often overlooked. Multiple questions such as where, when, and how bioenergy is deployed thus have not been sufficiently addressed within a single modeling framework. Moreover, techno-economic models that can capture the dependencies of bioenergy supply chain variables among end-use sectors still need to be explored. This thesis presents a spatially and temporally explicit techno-economic supply chain optimization model that allows the assessment of bioenergy deployment at a higher system level from a multi-sectoral perspective. This thesis also presents applications of the model in the context of developing low-carbon pathways for a developing country having an economy reliant on fossil fuels and agriculture, with Malaysia serving as a case study. The model was developed in the generic algebraic modeling system, with ArcGIS applied for spatial processing and Python applied for database management. The first part of the thesis presents the model application for assessing long-term cross-cutting impact of implementing bioenergy in multiple energy sectors up to 2050. The findings suggest that integrating substantial capacity of bioenergy in Malaysia's energy sectors could help save up to 37% of the annual emission avoidance cost of meeting the long-term emission target. The findings also suggest that the renewable energy policies could deliver more emission reductions than the decarbonization policies, but would require 30% more cumulative investment. The second part of the thesis discusses more detailed strategies on how biomass co-firing with coal can contribute to meeting short-term emission target up to 2030, which is related to multi-scale production of solid biofuels from palm oil biomass to scale up co-firing. The findings show that densified biomass feedstock could substitute significant shares of coal capacities to deliver up to 29 Mt/year of greenhouse gas reduction. Nevertheless, this would cause a surge in the electricity system cost by up to 2 billion USD/year due to the substitution of up to 40% of the coal-fired plant capacities. The third part of the thesis presents the model application to analyze the impact of the co-deployment of co-firing and dedicated biomass technologies in contributing to the bioenergy cost reduction under the impact of incremental decarbonization targets and supply chain cost parameter variations. The findings suggest that the multi-sectoral deployment of bioenergy in energy systems is key to meeting decarbonization targets at the national scale. By also considering biomass co-firing with coal in the biomass technological pathway, up to 27% of bioenergy cost reduction could be enabled in the main case. All the findings from this thesis are expected to inform the ongoing policies and initiatives regarding greenhouse gas reduction, renewable energy production, and resource efficiency improvement for managing environmental sustainability.