Distributionally Robust Decision-Dependent Generation and Transmission Expansion Planning for 100% Renewable Energy Utilization

Abstract

Renewable energy is the fundamental approach to reducing the carbon emission of power systems. To fully utilize renewable energy sources in the planning stage, a co-optimization of generation and transmission expansion planning (G&TEP) strategy is proposed, where the system security is guaranteed under uncertainties. To address the long-term uncertainties of loads, renewable energy output, and component failures across the planning horizon, a novel decision-dependent ambiguity set is proposed using total variation distance, where the renewable energy output and component failures are affected by planning. A multi-year G&TEP problem is formulated as a two-stage distributionally robust optimization problem with decision-dependent ambiguity sets, where the renewable energy, energy storage systems (ESSs), and transmission lines are jointly optimized. Using Lagrange duality, this problem is further reformulated as a mixed-integer linear programming problem, which can be solved by the off-the-shelf solvers. The simulations are performed on a modified Garver 6 test system. The effectiveness of the proposed strategy is verified by the numerical results.