Perpustakaan Digital ITB

In 2022, global carbon dioxide emissions from energy sources increased by 0.9% to over 36.8 billion metric tons, with Indonesia contributing 39.3 million tons (IEA, 2022). The Indonesian government's target to reduce emissions by 29% below business-as-usual levels by 2030 is not aligned with a 1.5°C pathway, necessitating effective emission reduction strategies. Carbon Capture and Storage (CCS) and Carbon Capture, Utilization, and Storage (CCUS) are promising solutions given Indonesia's significant potential, including numerous depleted fields with a total CO2 storage capacity of 12.1 billion tons. However, high costs and data uncertainties pose challenges to efficient CCS/CCUS implementation. Focusing on East Java, which has multiple CO2 sources and depleted fields suitable for storage, this study aims to optimize the CO2 transport and storage network. East Java's existing infrastructure and available field data make it an ideal case for demonstrating the potential of CCS/CCUS to reduce emissions effectively and economically. This study builds a network optimization model considering uncertainties in the CCS/CCUS value chain. It evaluates the planning of CCS/CCUS hubs in Indonesia, particularly in East Java, while accounting for uncertainty. Additionally, it seeks to understand the impact of uncertainty on optimization results, cost estimates, and solutions derived from hub design. The study employs a Mixed-Integer Linear Programming (MILP) model integrated with Monte Carlo simulation and Latin Hypercube Sampling (LHS) to optimize CO2 transport and storage networks. Initially, the model is benchmarked deterministically against tools like SimCCS and Sequestrix. Subsequently, uncertainties in key parameters such as CO2 prices, oil prices, capture rates, and capital and operational expenditures are incorporated using LHS. The model runs multiple iterations to account for these uncertainties, and the results are analyzed probabilistically to identify optimal network configurations. Deterministic analysis validates the model’s consistency and accuracy. Probabilistic analysis shows that incorporating uncertainties significantly enhances the model's flexibility and robustness. At the P50 probability level, two distinct network configurations are identified, while at the P90 level, a single optimal configuration emerges. Key metrics, such as the number of active sinks and sources, mass injection rates, pipeline diameters, and the number of pumps required, are evaluated across different scenarios. Cost analyses for storage, capture, and transport highlight the economic implications of various configurations. The Monte Carlo simulation results provide a comprehensive understanding of potential outcomes, emphasizing the importance of considering variability in strategic planning. This study introduces several novel aspects to CCS and CCUS network optimization by considering multiple CO2 sources and sinks (multisource and multisink) and integrating uncertainty analysis. The model identifies the number of pumps needed and the intervals for pump installation, providing detailed cost and income analysis from each entity’s perspective. The developed optimization model adjusts the timing of when a source or sink is active, running on a per-time-step basis to observe the annual profile. These contributions significantly advance the economic viability and resilience of CO2 management projects, offering valuable insights for strategic decisions in the petroleum industry.