Sizing optimization of Hybrid Stand Alone Photovoltaic system

Abstract

This paper presents a sizing optimization technique for Hybrid Stand-Alone Photovoltaic (HSAPV). In this study, two optimization techniques based on the mechanics of Evolutionary Programming (EP) have been developed, namely Fast Evolutionary Programming (FEP) and Classical Evolutionary Programming (CEP). These techniques have been integrated into the sizing process to maximize the technical performance of the HSAPV system. It is used to determine the optimum PV modules, charge controllers, inverters, battery and diesel generator. These variables are used as the control parameters to maximize the expected performance ratio (PR) of the HSAPV system. Comparative studies with respect to a benchmarking technique namely the Iterative Sizing Algorithm (ISA) were conducted in order to reveal their merit in terms of achieving maxima PR value and minimal computation time. Results obtained from the study exhibited that FEP outperformed CEP. The developed FEP and CEP also demonstrated comparatively fast with respect to ISA as the benchmark technique.