3.2 Evaluation of Demand-Side Flexibility and Distributed Energy Assets
The rapid concentration of computational loads from artificial intelligence infrastructure poses critical challenges to transmission stability across South Korea. Addressing this demand surge requires harmonizing theoretical principles of grid flexibility with empirical dispatch mechanisms. On one hand, production cost simulations and generative modeling frameworks indicate that power systems must continuously quantify flexibility requirements to prevent operational shortfalls during peak demand spikes (IEEE SPET, 2025). This theoretical necessity translates directly into practical implementation when assessing distributed energy assets. Specifically, data-driven assessments of vehicle-to-grid capabilities in Korea demonstrate that distributed mobile storage can provide crucial demand-side flexibility, acting as an adaptive buffer against high-density industrial and computational loads (Sustainability, 2023). However, mobilizing such decentralized flexibility requires comprehensive data governance across network operators. Intelligent power grid data catalog architectures provide the foundational data structures needed to coordinate disparate demand-side assets with centralized transmission schedules in real time (IEEE SGAI, 2025). By comparing centralized simulation models with decentralized distributed assets, it becomes evident that hardware-level capacity additions alone cannot resolve localized network congestion. Instead, integrating flexible demand response with intelligent data cataloging creates an interconnected operational framework capable of stabilizing Korea's grid amid escalating data-centre energy consumption.