Adaptive Distributionally Robust Optimization for Electricity and Electrified Transportation Planning

Abstract

An increased penetration of plug-in electric vehicles (PEVs) in the transportation sector will raise electricity demand inevitably, while dramatically changing its temporal and spatial patterns. This will result in more reliability stress and security constraints on existing and planned electricity generation and transmission assets, at a time utilities are facing pressure to do more with less. This paper proposes to address grid limitations, which may constrain large-scale PEV integration in future grids, using a novel planning model for transmission and generation expansions under uncertainties, based on adaptive robust optimization via scenario-based ambiguity sets. The new model can achieve decarbonization of both the electricity and transportation sectors economically, while meeting the electric grid reliability requirements. It also incorporates PEV charging controls over the planning horizon aiming at facilitating the maximum PEV penetration in the used test cases, more economically and realistically, considering current technology trends. Successful applications of the proposed model on a 118 bus test system and the simplified Ontario’s transmission grid demonstrate, in a satisfactory manner, its capability and effectiveness for planning deep decarbonization technology paths.