(29b) Renewables-Assisted Flexible Carbon Capture: A Dynamic Optimization Framework for Transitioning Towards Clean Energy

Fossil fuels such as coal, petroleum and natural gas account for two-third of the total electricity generated in the US. However, these energy sources are carbon-intensive and lead to considerable anthropogenic CO₂ emissions. The use of CO₂ capture and storage (CCS) and the integration of renewable energy sources with electricity grids are two promising technologies that can substantially reduce fossil fuel-based emissions from power generation. However, there are several technological challenges that need to be addressed before their widespread deployment in the power sector. Specifically, the intermittent availability of renewable energy requires cost-intensive grid modifications [1,2]. Similarly, CCS has high energy intensity and can lead to significant reduction in the net power plant output (25-40%) [3]. Typically, these challenges are considered and addressed independently at the large-scale grid-level [4-7], thereby leading to system overdesign and limited operational flexibility for both renewables and CCS. We explore the synergies between the operation of renewables and CCS to address their integration challenges with power grids. Particularly, we consider flexible solvent-based CCS operation with time-varying capture system load and solvent buffer tanks. In the proposed scheme, renewables provide emission-free energy for CCS and flexible CCS acts as an energy storage to counter renewable intermittency. The energy-intensive capture operation load can be reduced during peak electricity demand periods to supply energy to the grid [8]. Additionally, the excess renewable energy can be utilized for CO₂ capture and solvent regeneration during off-peak periods.

A mathematical optimization-based decision framework is developed to evaluate if the benefits obtained from integrating renewables and flexible CCS with existing power plants outweigh their capital cost under spatiotemporal variability of electricity markets and renewable energy. The overall optimization framework is based on a mixed integer linear programming (MILP) model for maximizing the net present value (NPV) of the integrated system. We propose a two-stage solution strategy to effectively solve the large-scale design and scheduling problem. To begin with, we apply a scenario reduction technique that re-assigns the scenario frequencies and obtain the representative time-aggregated scenarios of solar and wind availability, and electricity price. This reduced scenario set is used for computing the first-stage design designs. Next, the second stage operational decisions are determined by fixing this set of design decisions and solving using the original time-varying scenarios. The two-stage strategy effectively decouples the complicating long-term design decisions from the short-term operating decisions to strike a balance between computational tractability and solution quality. We demonstrate this framework through extensive nationwide as well as statewide (Texas) analysis for the integration of wind turbine, solar photovoltaic (PV) systems and a flexible CO₂ capture unit with existing coal-fired power plants across the US [9]. The economic incentive to adopt carbon reduction techniques is provided by imposing a regulatory policy including a carbon tax as well as carbon credits. Our analysis shows that it is profitable to integrate solar PV-powered CO₂ capture with nearly one-third of the coal plants in the US and 13 out of the 14 power plants in the state of Texas for a carbon tax above $80/ton, carbon credits price above $35/ton and solar PV investment cost below $0.3/W. For power plants with optimal solar PV integration, the solar farm size increases with power plant nameplate capacity with the average being 25.8% of the nameplate capacity. Additionally, the integrated system cost-effectively counters the renewable intermittency with the help of flexible CCS operation, and avoids an investment in equivalent battery storage amounting to 4.4 times the installed cost of the solar PV farm. Furthermore, the levelized cost of electricity (LCOE) of the integrated system is less than that of a new NGCC plant with achieved CO₂ emission reduction ranging between 87.5%-91%. This indicates it is more economical for coal plant operators to invest in the integrated system as compared to replacing the coal plant with a natural gas-based plant to meet CO₂ emission targets. The integrated system of coal plants retrofitted with renewables-assisted flexible CCS thereby paves the way to a cost-effective clean energy future for the current fossil-dominated energy landscape.

References:

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