Abstract:

The OPF techniques may be broken down into two broad categories: Intelligent and Conventional. The prominent Newton method, Gradient method, Quadratic Programming method, Interior point method, and Linear Programming method are only few of the typical procedures. OPF seeks to maximize some criterion within the constraints of the network power flow equations and the capabilities of the system and its components. By changing the available controls to minimize an objective function under strict operational and security constraints, the ideal situation is achieved. This chapter discusses the methods already in use and those that have been suggested to address the OPF issue. Formulation of the OPF issue, restrictions, objective function, applications, and detailed reporting of many well-known OPF approaches are all part of this. Particle swarm optimization and the Genetic Algorithm are two examples of the recently created and widely used approaches that are part of intelligent techniques. In this study, both evolutionary and metaheuristic algorithms are taken into account to analyze optimum power flow. PSO and GA are preferred over single-point methods like simulated annealing and tabu search due to the multi-parent effect they produce. When compared to existing metaheuristic algorithms, the Bat method performs much better across a variety of use cases.

Description:

Optimisation Methodologies, Optimal Power Flow, OPF techniques, Linear Programming method