  We present a distributionally robust optimization (DRO) approach for the
transmission expansion planning problem, considering both long- and short-term
uncertainties on the system demand and non-dispatchable renewable generation.
On the long-term level, as is customary in industry applications, we address
the deep uncertainties arising from social and economic transformations,
political and environmental issues, and technology disruptions by using
long-term scenarios devised by experts. In this setting, many exogenous
long-term scenarios containing partial information about the random parameters,
namely, the average and the support set, can be considered. For each long-term
scenario, a conditional ambiguity set models the incomplete knowledge about the
probability distribution of the uncertain parameters in the short-term
operation. Consequently, the mathematical problem is formulated as a DRO model
with multiple conditional ambiguity sets. The resulting infinite-dimensional
problem is recast as an exact, although very large, finite mixed-integer linear
programming problem. To circumvent scalability issues, we propose a new
enhanced-column-and-constraint-generation (ECCG) decomposition approach with an
additional Dantzig--Wolfe procedure. In comparison to existing methods, ECCG
leads to a better representation of the recourse function and, consequently,
tighter bounds. Numerical experiments based on the benchmark IEEE 118-bus
system are reported to corroborate the effectiveness of the method.
