Optimisation and planning of biomass supply chain for new and existing power plants based on carbon reduction targets

Abstract

The biomass energy sector in most developing countries is not well established due to the high cost of implementation and lack of quantitative planning tools. This paper addresses this issue by presenting a quantitative planning tool used to plan and optimise biomass supply chains based on carbon reduction targets. The tool proposed in this work centres on three important stages: Carbon Emission Pinch Analysis (CEPA), mathematical optimisation, and multi-stakeholder analysis. CEPA is used to determine the minimum amount of biomass to achieve carbon reductions. Then, mathematical optimisation is used to optimise the biomass supply chain based on the carbon reduction target. The optimisation step considers the use of new biomass power plants and co-firing existing power plants. Following this, the importance of power plants within the optimal biomass supply chain is identified using a multi-stakeholder analysis method known as Shapley-Shubik power index. Shapley-Shubik power index is used to determine the level of power held by each power plant in the biomass supply chain based on the number of times each stakeholder become pivotal in achieving several quotas (i.e., power purchase agreement). A case study comprising a biomass supply chain in Malaysia is solved to demonstrate the presented tool. The case study analyses several scenarios in which the biomass supply chain can be deployed. Case study results indicated that co-firing existing power plants did indeed minimise capital and operating expenditure. Conversely, the cost of transporting biomass may be high depending on the location of these plants. Boiler systems were the most chosen due to their high biomass-to-power conversion rates. The multi-stakeholder analysis determined that a power plant with higher power output, cheaper costs, and superior biomass-to-power conversion rate often appears as the more significant player in the supply chain.