  The necessary decarbonization efforts in energy sectors entail the
integration of flexibility assets, as well as increased levels of uncertainty
for the planning and operation of power systems. To cope with this in a
cost-effective manner, transmission expansion planning (TEP) models need to
incorporate progressively more details to represent potential long-term system
developments and the operation of power grids with intermittent renewable
generation. However, the increased modeling complexities of TEP exercises can
easily lead to computationally intractable optimization problems. Currently,
most techniques that address computational intractability alter the original
problem, thus neglecting critical modeling aspects or affecting the structure
of the optimal solution. In this paper, we propose an alternative approach to
significantly alleviate the computational burden of large-scale TEP problems.
Our approach integrates machine learning (ML) with the well-established Benders
decomposition to manage the problem size while preserving solution quality. The
proposed ML-enhanced Multicut Benders Decomposition algorithm improves
computational efficiency by identifying effective and ineffective optimality
cuts via supervised learning techniques. We illustrate the benefits of the
proposed methodology by solving a number of multi-stage TEP problems of
different sizes, based on the IEEE24 and IEEE118 test systems, while also
considering energy storage investment options.
