Standalone Photovoltaic System with Maximum Power Point Tracking: Modeling and Simulation

1. Introduction

In the world of today, the continuous provide of electric power plays an important role in reaching economic and social rapid development in countries (Arezou et al., 2017), which causes energy and environment crisis. This requires the reduction of the dependence on traditional energy and the improvement of the level of the development and the utilization of the renewable energy sources gradually. The Renewable energy has an advance all over the world in the environment protection, thanks to the features of non-polluting and large reserve etc. (Yatimi & Aroudam, 2016a), in particular photovoltaic solar energy. PV systems have been widely utilized in various applications, such as battery charging, water pumping (Hamrouni et al., 2009), home power supply etc., to convert the solar energy to electrical energy through the semiconductor devices called photovoltaic cells based on photovoltaic effect (Yatimi & Aroudam, 2015). The PV system is a non-linear system, and like many other systems requires a continuous control to work performantly. In this context, many control systems are designed in the literature (Lazaros et al., 2017) in order to operate under strict specifications, to satisfy certain aims, like safety regulations in the industry, optimal production of PV panels, level control in chemical processes and many more. On the other hand, the output characteristic of PV module is nonlinear and changes with temperature and solar irradiance. Therefore, its maximum power point is not constant. Under each condition PV cell has a point at which it can produce its MPP. Hence, the use of MPPT techniques to uphold the PV module operating at its MPP and then to increase the PV system efficiency is crucial.

In recent years, a variety of techniques have been proposed for tracking the MPP of PV systems. MPPT (see meaning in Box 1) techniques are varying between them in many aspects, including simplicity, convergence speed, hardware implementation, sensors required and cost. For example, the Perturb and Observe (P&O) method (Prabaharan & Palanisamy, 2016), which is the most widely used algorithm due to the simplicity of structure and the ease of implementation, but has limitations, it can work well when the solar irradiance and the temperature do not vary quickly with time and the output power is oscillating around the MPP. The Incremental Conductance (IC) method (Rania et al., 2017; Kashif et al. 2014), which has better performance than the P&O method. The main advantage of IC method is that it can offer good performance under rapidly changing atmospheric conditions in addition to its ability to achieve lower oscillation around MPP.