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The increasing penetration of variable renewable energy (VRE) has broughtsignificant challenges for power systems planning and operation. These highlyvariable sources are typically distributed in the grid; therefore, a detailedrepresentation of transmission bottlenecks is fundamental to approximate theimpact of the transmission network on the dispatch with VRE resources. The finegrain temporal scale of short term and day-ahead dispatch, taking into accountthe network constraints, also mandatory for mid-term planning studies, combinedwith the high variability of the VRE has brought the need to represent theseuncertainties in stochastic optimization models while taking into account thetransmission system. These requirements impose a computational burden to solvethe planning and operation models. We propose a methodology based on communitydetection to aggregate the network representation, capable of preserving thelocational marginal price (LMP) differences in multiple VRE scenarios, anddescribe a real-world operational planning study. The optimal expected costsolution considering aggregated networks is compared with the full networkrepresentation. Both representations were embedded in an operation modelrelying on Stochastic Dual Dynamic Programming (SDDP) to deal with the randomvariables in a multi-stage problem.

Community Detection for Power Systems Network Aggregation Considering Renewable Variability