  Solving large-scale capacity expansion problems (CEPs) is central to
cost-effective decarbonization of regional-scale energy systems. To ensure the
intended outcomes of CEPs, modeling uncertainty due to weather-dependent
variable renewable energy (VRE) supply and energy demand becomes crucially
important. However, the resulting stochastic optimization models are often less
computationally tractable than their deterministic counterparts. Here, we
propose a learning-assisted approximate solution method to tractably solve
two-stage stochastic CEPs. Our method identifies low-cost planning decisions by
constructing and solving a sequence of tractable temporally aggregated
surrogate problems. We adopt a Bayesian optimization approach to searching the
space of time series aggregation hyperparameters and compute approximate
solutions that minimize costs on a validation set of supply-demand projections.
Importantly, we evaluate solved planning outcomes on a held-out set of test
projections. We apply our approach to generation and transmission expansion
planning for a joint power-gas system spanning New England. We show that our
approach yields an estimated cost savings of up to 3.8% in comparison to
benchmark time series aggregation approaches.
