Generation and Transmission Expansion Planning Using a Nested Decomposition Algorithm

This work presents an implementation of a Nested Decomposition Algorithm (NDA) applied to co-optimizing generation and transmission capacity expansion planning problems in power systems, including operational flexibility constraints. The proposed methodology has been gaining relevance in recent years, as it can efficiently solve large mixed-integer problems faster than the conventional extensive formulation (mixed-integer linear programming). Three case studies are conducted on two IEEE test power systems to evaluate the algorithm’s performance and cut configuration. The first case study compares the performance between the NDA and the extensive formulation. The second case study compares the performance of each cut type, analyzing differences in simulation times and algorithm convergence. The third case study proposes a set of cut patterns based on the prior outcomes, whose performance and convergence are tested. Based on the simulation results, conclusions are drawn about the capability and performance of the NDA applied to the capacity expansion planning problem. The study shows that obtaining results with reasonable convergence in less simulation time is possible using a particular pattern.