New York State's 100% renewable electricity transition planning under uncertainty using a data-driven multistage adaptive robust optimization approach with machine-learning

Power system decarbonization is critical for combating climate change, and handling systems uncertainties is essential for designing robust renewable transition pathways. In this study, a bottom-up data-driven multistage adaptive robust optimization (MARO) framework is proposed to address the power systems' renewable transition under uncertainty. To illustrate the applicability of the proposed framework, a case study for New York State is presented. Machine learning techniques, including a variational algorithm for Dirichlet process mixture model, principal component analysis, and kernel density estimation, are applied for constructing data-driven uncertainty sets, which are integrated into the proposed MARO framework to systematically handle uncertainty. The results show that the total renewable electricity transition costs under uncertainty are 21%-42% higher than deterministic planning, and the costs under the data-driven uncertainty sets are 2%-17% lower than the conventional uncertainty sets. By 2035, on-land wind and offshore wind would be the major power source for the deterministic planning case and robust optimization cases, respectively.

Automated summary

Power system decarbonization is crucial for combating climate change, and addressing systems uncertainties is essential for designing robust renewable transition pathways. A data-driven multistage adaptive robust optimization (MARO) framework is proposed in this study to handle renewable transition under uncertainty, using machine learning techniques to construct data-driven uncertainty sets. The results show higher total renewable electricity transition costs under uncertainty compared to deterministic planning, but utilizing data-driven uncertainty sets can lower the costs.