As the penetration of renewable energy increases, the use of high-resolution time-series data becomes indispensable in long-term power mix planning. This study employs a chronology-based fitting approach to evaluate the accuracy and computational efficiency of reduced time-series in Jeju’s power mix simulations. To address the inherent limitations of conventional reduction techniques, an Integrated Chronology Framework is proposed that explicitly reflects the distinct temporal characteristics of load, solar, and wind resources. The proposed framework preserves critical chronological patterns while ensuring computational tractability, which is essential in systems with high renewable variability. Future research will focus on conducting comprehensive sensitivity analyses across diverse operational scenarios and extending the methodology to mainland data, thereby strengthening the robustness and applicability of long-term power mix planning in support of sustainable energy transition.
Ryu et al. (Fri,) studied this question.