On Evaluating Power Loss with HATSGA Algorithm for Power Network Reconfiguration in the Smart Grid
This addresses power loss optimization for smart grid management, but appears incremental as it combines existing methods.
The paper tackles power network reconfiguration in smart grids using the HATSGA algorithm, a hybrid of Tabu Search and Genetic Algorithm, and finds it can compute efficient solutions with minimal power loss in feasible computational time on the IEEE 14-Bus test scenario.
This paper presents the power network reconfiguration algorithm HATSGA with a "R" modeling approach and evaluates its behavior in computing new reconfiguration topologies for the power network in the Smart Grid context. The modeling of the power distribution network with the language "R" is used to represent the network and support the computation of distinct algorithm configurations towards the evaluation of new reconfiguration topologies. The HATSGA algorithm adopts a hybrid Tabu Search and Genetic Algorithm strategy and can be configured in different ways to compute network reconfiguration solutions. The evaluation of power loss with HATSGA uses the IEEE 14-Bus topology as the power test scenario. The evaluation of reconfiguration topologies with minimum power loss with HATSGA indicates that an efficient solution can be reached with a feasible computational time. This suggests that HATSGA can be potentially used for computing reconfiguration network topologies and, beyond that, it can be used for autonomic self-healing management approaches where a feasible computational time is required.