Backtracking Search Algorithm With Single and Multi-Objective Function for the Solution of Optimal Power Flow Problem

Introduction

It is a prime goal of the power system engineer to ensure economic and secure functioning of a specific power network. Since 1980, deregulation has been taking place over the world to overcome different challenges of the power system such as economic power generation, capacity shortage, transmission congestion, power outages, transmission line losses, and environmental issues. Apart from these challenges, active power generation cost is also affected by some practical issues such as valve-point loading and prohibited zone (Mahdad et al. 2010; Mukhrjee and Mukherjee, 2016). The optimal power flow method takes a major decisive role in order to address these challenges. Principally, the OPF method optimizes the control variables, considering some mathematical constraints (Abido, 2002; Abou et al. 2009). The OPF method is a multimodal iterative method, minimizes the non-linear, non-convex objective of a particular problem. OPF method deals with both single objective and multi-objective function. A multi-objective OPF method offers a more pragmatic perspective in view of the real world power system requirements. For instance, optimization of active power generation cost causes undesired bus-voltage profile. This drawback is minimized in multi-objective optimization, where different conflicting objectives are simultaneously optimized.

Techniques to solve OPF method are fallen into two groups. The first group is conventional optimization techniques and the second one is the evolutionary algorithms. Some conventional optimization techniques are newton method (Vincovic and Mihalic, 2009), linear programming (LP) (Al-Muawesh and Quamber, 2008), nonlinear programming (NLP) (Momoh et al. 1999). But these conventional techniques show some limitations in solving non-linear, non-convex, highly constrained, discrete optimization problems.