Robust transmission network expansion planning based on a data-driven uncertainty set considering spatio-temporal correlation

The increasing integration of variable renewable energy resources (RES) has introduced numerous uncertainties that threaten the secure and economical operation of electric power systems. Addressing these uncertainties is an essential aspect of transmission expansion planning (TEP). However, presently available robust TEP models consider spatial correlation but ignore temporal correlation among RES outputs, and fail to take advantage of the extensive real-world data available for reducing the conservatism of solutions. This paper addresses these issues by proposing a two-stage adaptive robust TEP model that accounts for extensive variation in RES outputs and loads under massive uncertainties based on a novel data-driven uncertainty set that contains an infinite number of realizations of uncertainties, including load and RES output, over 24 h in a single generic day. The uncertainty set can be constructed with any arbitrary confidence level based on the first and second order moment information of RES outputs and network loads that are extracted from massive historical data, in conjunction with Cholesky decomposition and a standard normal distribution table. The proposed TEP model is solved using an enhanced column and constraint generation method, where the subproblem is solved using an iterative method operating between the middle and lower levels. Case studies involving simulations of test systems with three different sizes demonstrate that the proposed method can effectively take into account the most likely scenarios to be realized under the preset confidence level, while also obtaining robust planning solutions with an affordable computational time. Specifically, with the number of uncertain variables increasing from 6 in the Garver-6 test system to 91 in the HRP test system, the error between the actual confidence level and the preset confidence level of uncertainty set falls from 12.71% to 2.93%.