Community Detection for Power Systems Network Aggregation Considering Renewable Variability
This addresses computational challenges for power system operators and planners dealing with renewable variability, though it is incremental as it builds on existing aggregation and optimization methods.
The paper tackles the computational burden in power systems planning due to variable renewable energy by proposing a community detection method for network aggregation, showing that the aggregated representation preserves locational marginal price differences and yields comparable expected costs to the full network model.
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.