Metaheuristic Optimization for Transmission Network Expansion Planning: Testbed 2 of the Competition on Evolutionary Computation in the Energy Domain

Abstract

The complexity of the transmission network expansion planning (TNEP) problem has been increasing due to the new constraints given by renewable generation uncertainty, new market rules and players, and the continuous demand growth with the introduction of electric vehicles and energy storage systems. The problem consists of finding the optimal number and location of new transmission lines to support the demand, which can be extremely hard to optimize. As such, in this paper, we focus on metaheuristic optimization to solve a TENP problem proposed in testbed 2 of the 2023 competition on evolutionary computation in the energy domain. The 87-bus north-northeast Brazilian transmission system is considered for the case study, and different DE metaheuristics are used for the optimization process. Results show that the HyDE algorithm presents the overall best performance when compared to other DE strategies. HyDE is able to achieve the overall lowest costs with a reduction of around 67% compared to L-SHADE.