Spatial biomass resource planning framework for co-firing under carbon policy scheme

Effective spatial planning is crucial for the cost-effectiveness and sustainable development of biomass energy resources due to the diffuse nature of biomass and high transportation cost. To leverage the existing capitals of the fossil fuels energy systems, portions of biomass can be integrated as fuel within the existing energy facilities through co-firing technology. Although biomass co-firing operates at a low retrofitting cost environment, this does not eliminate all the associated cost required in supplying the biomass to the power generation facilities. This paper presented the development of a spatial biomass resource planning framework which integrates several modelling tools such as Geographical Information System (GIS), Analytic Hierarchy Process (AHP) and Mixed-Integer Linear Programming (MILP) to investigate the level of carbon prices needed to support co-firing implementation in Malaysia in 2020. The results have been showing that carbon price range of 3 - 12 USD/t can be imposed by Malaysia in order to achieve the future national renewable and environmental targets while reducing the coal-based industrial emissions of up to 19.75 %

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

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

Modeling near-term bioenergy strategy to meet the emission target production of solid biofuel to scale up co-firing

The present work presented the application of a spatio-temporal techno-economic supply chain model for the modeling of near-term bioenergy strategy to meet the emission target through the co-firing of biomass with coal. Multi-level solid biofuel production capacities for the purpose of scaling up the maximum co-firing share in coal plants were incorporated into the studied supply chain configuration. Scenarios related to the near-term CO2 emission target were developed and analyzed. The findings have shown that higher commitment of emission reduction will impact the choices of the biomass pre-treatment technologies and the scales needed. Co-firing is capable of contributing toward the achievement of more ambitious emission reduction than the targeted but highlighting the need for policy to financially supporting the near-term deployment

Integrating palm oil biomass waste utilization in coal-fired power plants for meeting near-term emission targets

Biomass co-firing with coal can be adopted in the electricity sector to promote greenhouse gas reduction, renewable energy production, and resource efficiency improvement toward environmental sustainability. This realization, however, requires effective management of supply chain issues, such as the collection of biomass feedstock, the transportation of biomass, and the localization of biomass processing plants to deliver the co-firing scales needed. This work addresses these issues by providing a techno-economic assessment conducted in a spatially-explicit manner to investigate the opportunity for scaling up the co-firing deployment at the national scale. The modeling approach is applied to the case of Malaysia's coal and palm oil biomass industries. The number of cases involving the impact of energy decarbonization targets, economic policy instrument, and supply chain cost parameter variations on the co-firing scales deployed are assessed. The findings show that densified biomass feedstock can substitute significant shares of coal capacities to deliver up to 29 MtCO2/year of carbon dioxide 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 plant capacities. In facilitating the maximal deployment of co-firing at the national scale, more than 100 solid biofuel production plants would need to be built to support a maximum of 41 TWh/year of co-firing capacity. Actions to minimize the specific cost elements of the biomass co-firing supply chain are thus needed in the near term to increase the effectiveness of economic policy instrument to promote co-firing and reduce environmental emissions

Spatial optimisation of oil palm biomass co-firing for emissions reduction in coal-fired power plant

Due to the rising concerns on climate issues, the transitions of fossil fuels to renewable energy are highly promoted globally. Malaysia which has abundant sources of biomass, is maximising the efforts to increase renewable energy shares in the current energy mix. Biomass co-firing with coal offers a promising route to less greenhouse gas (GHG) emissions due to the zero net greenhouse effect of biomass combustion. This paper presents an integrated spatial optimisation model of biomass co-firing supply chain for existing power generation facilities through the integration of geographical information system (GIS) and mixed-integer linear programming (MILP). The model integrates spatial distributions of biomass supply, locations to build biomass pre-treatment facilities, location-allocation of supply and demand of biomass co-firing supply chain and economic and environmental sounds of biomass co-firing system. The optimisation of the whole supply chain system is conducted with the aim to minimise the overall cost and its emissions while determining the most optimal locations to build pre-treatment facilities to support co-firing power generation. Based on the findings, the cost factors of deploying co-firing technology in existing coal-fired power plant are between 56.61 and 61.65 USD/MWh for 10–50% co-firing rates as compared to the base case electricity generation cost which is at 56.29 USD/MWh. Minimum differences in cost factors are achieved when dedicated fossil fuels scenario is compared to several co-firing scenarios. Up to 8.83 × 106 t of CO2 (equivalent to 46% of CO2 reduction) can be reduced annually in Johor as a result of this practice. This shows that co-firing technology is promising to be implemented in Malaysia while achieving significant emissions reduction target with incentives supported by government

Deploying bioenergy for decarbonizing Malaysian energy sectors and alleviating renewable energy poverty

Due to the capital cost of co-firing being lower than other biomass technologies, the transformation of coal plants into co-firing facilities can potentially minimize the bioenergy cost needed to meet energy decarbonization targets. This study analyzes the impact of the co-deployment of co-firing and dedicated biomass technologies in contributing to the bioenergy cost reduction for country-level energy systems using a spatio-temporal techno-economic optimization model. Malaysia is used as a case in the analysis. Different scenarios were developed to assess the robustness of the cost reduction potential under the impact of incremental CO2 reduction targets and supply chain cost parameter variations. Our results suggest that the multi-sectoral deployment of bioenergy in energy systems is key to meeting decarbonization targets at the national scale. By also considering co-firing in the biomass technological pathway, up to 27% of bioenergy cost reduction can be enabled in the baseline case. The decrease in the supply chain cost parameter values further enhances the cost reduction potential, bioenergy costs can be reduced up to threefold. The findings have shown that developing countries such as Malaysia can benefit from the use of their rich agricultural resources to cost-effectively alleviate renewable energy poverty