Optimal power flow-based approach for grid dispatch problems through Rao algorithms

Abstract

In order to fulfill the load demand and operational restrictions, the grid dispatch problem's optimization goal is to reduce fuel cost through variation of active and reactive power of the generators within fixed limits and to uplift the security of the power plants during voltage instability through proper allocation of static voltage compensator to ensure minimum power loss. This paper proposes a collaborative optimization of economic load dispatch and reactive power dispatch using three metaphor-less algorithms called Rao algorithms, where the current solution will interact with the finest and inferior solutions in the population, as well as a randomly selected solution. These strategies are free from any specific control parameters. These algorithms are formulated on the IEEE 30 bus system. The outcomes of the suggested techniques are compared with those of existing optimization approaches such as Bat algorithm (BOA), Modified Harmonic search algorithm (MSG-HS), Root tree optimization (RTO), Adaptive Gravitational search optimization (AGSO), Whale optimization algorithm (WOA), Fruit fly optimization (MOFOA), Crow search algorithm (CSA), Quasi-Oppositional Teaching learning based optimization (QOTLBO), and Jaya algorithm (JA) which demonstrates that the proposed strategies have great convergence characteristics and are the most systematic way to addressing these complicated power system issues. They are unable to converge on a local optimal solution prematurely while maintaining a high level of precision.