In this work, we propose a novel multi-scale bottom-up optimization framework to address the decarbonization transition planning for power systems, which incorporates multiple types of information for each existing or new unit in the power systems, including its technology, capacity, and age. To reduce the computational challenge, a novel approach integrating Principal Component Analysis (PCA) with clustering techniques is proposed to obtain representative days. To illustrate the applicability of the proposed framework, a case study for New York State was presented. The proposed approach obtaining representative days using PCA coupled with K-means shows better performance than multiple state-of-the-art clustering approaches. The optimization results indicate that offshore wind, hydro, and utility solar are the main power sources in the state by the end of the planning horizon. To validate the optimization results, we conduct hourly power systems operations simulation for the entire planning horizon, and the result indicate that the error bar using the proposed framework is less than 1.5% in the case study.
Keywords decarbonization, renewable electricity transition, multi-scale optimization, renewable generation, bottom-up model