Comparison of binary optimization techniques for real-time management of sustainable autonomous microgrid

Abstract

Sustainable autonomous microgrid is an integrated power ecosystem consisting of Distributed Generators (DGs), storage devices and loads. Such microgrids are expected to become an integral part of the future power system. Existence of intermittent renewable based sources, loads with different priorities and limited generation capacity makes power balancing in an autonomous microgrid a challenging task. During real-time implementation, desired reliability and stability is achieved in such an infrastructure by utilizing a fast acting algorithm for priority based load management and network reconfiguration. Primary task of the algorithm is to identify ON/OFF status of the load breakers and the tie/sectionalizing breakers in the system. As the breaker status is represented by ‘1’ or ‘0’, binary version of optimization techniques need to be used to find the optimum solution. In this paper, the Binary coded Genetic Algorithm (BGA) and Binary Particle Swarm Optimization (BPSO) is used in the algorithm for real-time management of a sustainable autonomous microgrid and their performances are compared. The results show that BPSO has outperformed BGA in obtaining the solution.