This chapter addresses several key issues related to the AI-based design of FCLs and their impact on modern power grid parameters. The first part explains the general logic behind FCL placement in a power system. This is followed by an AI-based approach to determine the optimal placement of FCLs in large-scale power networks. Additionally, the chapter explores the optimal design of FCLs, addressing the key principles behind various design methodologies. An illustrative example of optimal FCL placement and design is also provided. The chapter also focuses on cybersecurity and policy trends in modern smart grids, examining how integrating FCLs can influence cybersecurity measures and evolving grid policy directions.

Achieving the Paris Agreement's goal necessitates not only reducing carbon dioxide emissions to net zero but also actively removing CO₂ from the atmosphere. Direct Air Capture (DAC) emerges as a pivotal technology in this effort, offering a reliable, flexible, and scalable solution for negative emissions. However, DAC performance is highly sensitive to environmental factors such as temperature and humidity. Consequently, it is vital to develop dynamic control and optimization mechanisms that can enhance the cost-efficiency of DAC. Due to the complexity and lack of a comprehensive model for DAC systems, the need for expert knowledge for modeling, and high computational costs, traditional model-based methods are not feasible. Therefore, we suggest a model-free, data-driven optimization technique based on Bayesian optimization to enhance the productivity and cost-effectiveness of DAC.