Extreme Scenarios Based Data-Adaptive Probability Uncertainty Set for Distributionally Robust Transmission Expansion Planning

Abstract

Increasing penetration of renewable energy into power systems is the development trend of future energy systems. One of the main challenges is to plan the expansion scheme of transmission systems to accommodate uncertainties of wind power. In this letter, we propose a novel extreme scenarios (ESs) based data-adaptive probability uncertainty set for the transmission expansion planning problem. First, available historical data are utilized to identify data-adaptive ESs through the convex hull technology, and the probability uncertainty set with respect to the obtained ESs is then established, from which we draw the final expansion decision based on the worst-case distribution. The proposed distributionally robust transmission expansion planning (DRTEP) model can guarantee optimality of expected cost under the worst-case distribution, while ensuring feasibility of all possible wind power generation. Simulation studies are carried out on a modified IEEE RTS 24-bus system to verify the effectiveness of the proposed DRTEP model.