Optimal carbon-constrained energy planning with direct air capture technology

• Main Author: Tiu, Sean Elijah J.
• Format: text
• Language: English
• Published: Animo Repository 2022
• Subjects: Carbon sequestration; Chemical Engineering
• Online Access: https://animorepository.dlsu.edu.ph/etdm_chemeng/8
• Institution: De La Salle University

The umbrella of negative emissions technologies is one of the most important solutions in mitigating the effects of climate change. Past studies, particularly those involving multi-criteria decision analysis, show that one of the potentially highly scalable technology is direct air capture (DAC) technology. It involves the removal of CO2 from the atmosphere and its storage in underground reservoirs or its utilization in generating valuable products. To maximize the benefits of DAC in low-carbon energy systems, systematic planning using mathematical approaches is needed. This study developed two mathematical programming models to optimize the integration of a DAC system in a network with pre-existing energy sources. The first model was a crisp linear programming (LP) model that involves the minimization of external energy input subject to energy demand and CO2 footprint requirements. That model was extended to consider energy demands as fuzzy constraints, and CO2 footprint and external energy requirements as fuzzy objectives to produce a second fuzzy LP model. Case studies were used to test the crisp model, one of which utilized data and parameters adapted from a previous study on energy distribution networks with carbon capture and storage (CCS); the results of this case study present the optimal source-sink connections and indicate a 40% additional energy required from external sources. The external energy requirement is higher compared to the previous study due to the difference of carbon dioxide removal efficiency between CCS and DAC. The fuzzy LP model was tested with a single case study, where the results present the optimal source-sink connections and indicate a 79% additional energy needed. The increase in external energy input is due to the model attaining higher degrees of satisfaction for the energy demand and CO2 footprint decision variables. The model and results from this study show one way of how DAC can be integrated into a pre-existing energy network to reduce the net carbon footprint of a region.