A comparison of Improved Nature-Inspired Algorithms for Optimal Power System Operation

The influencing factors associated with the efficient operation of power systems are minimum fuel cost and losses in the transmission line. Optimal Power Dispatch (OPD) problem is treated to minimize instantaneous operating cost, incremental cost, and transmission line losses considering various network operating constraint. Newly developed Nature-inspired optimization algorithms approach are proposed in this analysis with robust parameter selections. The results of most popular Genetic Algorithm (GA) and based on swarm behavior Particle Swarm Optimization (PSO) are compared with four Nature-inspired metaheuristic algorithms of Cuckoo Search (CS), Bat Algorithm (BA), Flower Pollination Algorithm (FPA), and Firefly Algorithm (FA). The quadratic cost function of power generation and penalty function to account for inequality constraints on dependent variables are added for solving OPD problem. A common algorithms evaluation parameters such as population size and generation limit are designated on an equal scale. Explicit parameters for each algorithm are tuned properly for optimal operations. The algorithms are tested on IEEE-26 and IEEE-30 system. Analysis Outcomes obtained showcase the efficiency of each algorithms parametric turning improvement.