Data-driven robust transmission expansion planning against rising temperatures^*

Abstract The evidence of ongoing rising temperatures has been accumulating for years. It is, therefore, essential to utilize recent climate data to inform future investment decisions. Perhaps nowhere is this more important than in improving the aging power grid infrastructure. This work presents a novel data-driven robust optimization approach to guide transmission expansion planning and transmission capacity expansion planning decisions focused on mitigating the effects of globally and regionally rising temperatures. The proposed methodology is tested on a large-scale realistic test case of the power transmission grid in Arizona, and two classes of valid inequality are used to accelerate the computation time. The effects of temperature are modeled at a regional level in the feastured test case using a k -means clustering method. Results demonstrate that more accurate regional temperature modeling results in more focused investment plans.