Considering uncertainty in the fuel cell and capacitor allocation problem using a novel self adaptive modification approach

Abstract

Abstract

Optimal Fuel Cell (FC) allocation and Shunt Capacitor Placement (SCP) are two significant and precious strategies that can improve the system performance greatly. In order to make use of these two useful strategies efficiently, they should be investigated in one single framework simultaneously. This paper aims to solve the optimal allocation and sizing of FC and SCP problems for reducing the total system costs. In order to make the analysis practical, the uncertainties of the active and reactive loads are modeled using the probabilistic load flow based on point estimate method (PEM). In this way, each random variable is replaced by its appropriate probability density function to capture the uncertainty effects in the analysis. The problem investigated is a kind of discrete nonlinear and non-convex optimization problem that requires a powerful optimizer to escape from the several local optima. In this regard, this work suggests a new optimization method based on Krill Herd (KH) algorithm to solve the problem deeply. A new self-adaptive modification method is also proposed to improve the total search ability of the algorithm. The feasibility and satisfying performance of the proposed method is examined on the IEEE 69-bus test system.