Optimal Phase Selection Of Single-Phase Appliances In Buildings Using String-Coded Genetic Algorithm

Phase imbalance in buildings, primarily caused by single-phase loads and generation, leads to increased neutral current, voltage imbalance, reduced energy efficiency, and potential equipment damage. To address these challenges, an optimal phase selection method is proposed for single-phase loads and generation. This method integrates integer programming with a string-coded genetic algorithm (GA). The GA employs string encoding to represent phase connections. Initially, a Mixed Integer Programming (MIP) solver identifies an initial solution, which is subsequently transformed into a string to initialize the GA’s genes. Subsequently, the GA executes standard operations such as mutation, crossover, evaluation, and selection. Case studies demonstrate the efficacy of this method in achieving substantial load balancing. Notably, the identification of multiple solutions with identical objective function values renders this approach suitable for smart buildings equipped with energy management systems that participate in ancillary services between low-voltage and medium-voltage networks. This research pertains to the domains of computer science, power engineering, and energy informatics.