Stochastic Optimal Power Flow using a Paired-Bacteria Optimizer

Abstract

Optimal Power Flow (OPF) is an effective tool for dispatch planning of power systems, and the algorithms aiming to solve the OPF have been widely studied. Among these algorithms, the Evolutionary Algorithms (EAs) are a popular type, which have been investigated. However, EAs are also notorious for the intensive computation caused by a large amount of evaluations of the objective function required by all individuals. In this paper, a stochastic OPF problem is introduced to describe a more realistic power system, which relies on a large number of evaluations. In order to reduce the computational time, a Paired-Bacteria Optimizer (PBO) based on a single bacterium foraging model is adopted to solve this expensive optimization problem. The PBO has been tested on high dimensional multimodal benchmark functions and applied for a stochastic optimal voltage control and fuel cost reduction problem which is a crucial part of power system planning. The experiment results have shown that it has a more reliable performance than the widely applied EAs.