Data-driven distributionally robust unbalanced operation of distribution networks with high penetration of photovoltaic generation and electric vehicles

Abstract

The uncertainties associated with the large-scale integration of electric vehicles (EV) and renewable energy resources introduce new challenges to operating the unbalanced three-phase distribution networks. In this paper, a data-driven distributionally robust optimization framework is proposed for the operation of the distribution network considering the uncertainties associated with the interconnected EV fleets and solar photovoltaic (PV) generation. The proposed framework leverages the column-and-constraint generation (C&CG) approach to minimize the operation cost considering the worst-case probability distributions of PV generation, the available energy in EV fleets, the arrival and departure times of EV fleets as well as their minimum and maximum energy capacities. The proposed approach is applied to the modified three-phase unbalanced IEEE 34-bus and IEEE 123-bus networks. To evaluate the performance of the proposed distributionally robust optimization framework, the results are compared to those procured by solving scenario-based stochastic programming and robust optimization problems. Furthermore, the impact of vehicle-to-grid capability on the operation of the distribution network is investigated and the in-sample and out-of-sample performances of the proposed framework are evaluated.