Modeling and optimization of carbon-negative NGCC plant enabled by modular direct air capture. (1st July 2023) - Cheng, Pengfei; Thierry, David M.; Hendrix, Howard; Dombrowski, Katherine D.; Sachde, Darshan J.; Realff, Matthew J.; Scott, Joseph K.
Abstract: Natural gas combined cycle (NGCC) plants are the most prevalent source of electricity in the United States and are expected to continue playing a key role in the future energy market. However, they are also one of the main sources of CO 2 emissions in the power market. Recently, the authors proposed a retrofit design for an existing NGCC to allow power generation with negative CO 2 emissions by utilizing both post-combustion carbon capture (PCC) and direct air capture (DAC). The DAC unit captures CO 2 directly from the atmosphere using low-grade heat from the NGCC via a novel heat integration scheme. The heat integration scheme and modular DAC design introduce significant flexibility, allowing the system to focus on either power generation or CO 2 capture in response to electricity price changes. However, this creates a complex trade-off between the cost of DAC capacity and the enhanced operational flexibility it provides. In this work, we model the proposed retrofit systematically and co-optimize the design and operation of the system for an entire year under different electricity price signals and CO 2 prices. The DAC operations are modeled with a novel formulation to account for the sorbent dynamics in its temperature swing cycles. The solutions for the resulting large-scale mixed-integer linear program show that a large DAC unit is preferred in most situations. The extra flexibility leads to longer dispatch time and higher profits compared with the base NGCC. Highlights: Built optimization model for a novel integrated natural gas combined cycle (NGCC)-direct air capture (DAC) system. Co-optimized system design (DAC capacity) and hourly operations for a year. Modeled DAC sorbent adsorption cycles with novel formulations based on experiments. Generated optimal solutions in 36 future energy scenarios with different time-varying electricity price signals and CO2 prices.