Dynamic modeling and optimal control schemes for an offshore-wind powered direct air capture system with energy storage options

Direct air capture of CO₂ is a technically feasible solution for reducing atmospheric CO₂ concentrations at-scale, building on decades of global research. However, powering such systems with CO₂-intensive fossil fuels results in reduced net CO₂ capture. This paper proposes an offshore-wind energy powered atmospheric CO₂ capture system. A key challenge is the variable nature of renewable wind-energy to meet direct air capture (DAC) system power requirements. One solution to mitigate this challenge is to integrate the wind-CO₂ capture system with advanced Energy Storage Systems (ESS). Previous research in this direction has been carried out, however the optimal ESS is still an open question due to the limitations and constraints of each ESS technology. The constraints include concerns over degradation, ESS response times, and overall costs. This paper proposes an advanced energy management strategy (EMS) within the CO₂ capture system to address the outlined problems. It presents a dynamic model for offshore-wind direct air capture of CO₂ system coupled with a battery-based energy storage system with the objective of maximizing CO₂ removal from air while fulfilling the overall system operational constraints and dynamics. More specifically, the proposed model investigates the flexibility of the CO₂ capture system with respect to wind power supply in different seasons. The DAC models proposed in this research consists of a three state automaton, namely: OFF, Adsoprtion, and Desorption which handle the three operational states of the proposed CO₂ capture system. In order to maximally utilize wind power availability, each operational state of the proposed model is considered as a separate load and dispatched separately. The proposed approach is then compared and analyzed by scheduling the system as a whole. Numerical results illustrate the feasibility of powering CO₂ capture system via variable wind power.