A Methodology for Calculating Representative Solar Curves Based on Multi-year Weighted Library and Optimism Frequency Control

As renewable energy expands, selecting representative solar profiles is crucial for power system planning. However, conventional methods based on central tendencies often smooth out intermittency, leading to an optimism bias that overestimates generation availability. To address this, this paper proposes a conservative profile generation method utilizing a multi-year weighted library and asymmetric loss functions. The proposed algorithm explicitly penalizes the frequency of over-estimation errors to mitigate the risk of phantom energy estimation. Case studies using 2017–2022 data from Korean sites verify that the proposed method reduces optimistic errors and maintains characteristic profile shapes compared with conventional mean-based and clustering-based baselines. By effectively capturing the lower bound of generation under high-variability conditions, such as spring intermittency and summer monsoons, the proposed approach provides a robust baseline for securing grid reliability.