  The increasing penetration of variable renewable energy (VRE) has brought
significant challenges for power systems planning and operation. These highly
variable sources are typically distributed in the grid; therefore, a detailed
representation of transmission bottlenecks is fundamental to approximate the
impact of the transmission network on the dispatch with VRE resources. The fine
grain temporal scale of short term and day-ahead dispatch, taking into account
the network constraints, also mandatory for mid-term planning studies, combined
with the high variability of the VRE has brought the need to represent these
uncertainties in stochastic optimization models while taking into account the
transmission system. These requirements impose a computational burden to solve
the planning and operation models. We propose a methodology based on community
detection to aggregate the network representation, capable of preserving the
locational marginal price (LMP) differences in multiple VRE scenarios, and
describe a real-world operational planning study. The optimal expected cost
solution considering aggregated networks is compared with the full network
representation. Both representations were embedded in an operation model
relying on Stochastic Dual Dynamic Programming (SDDP) to deal with the random
variables in a multi-stage problem.
