Adaptive fuzzy control with an optimization by using genetic algorithms for grid connected a hybrid photovoltaic-hydrogen generation system

The integration of significant amounts of renewable-storage hybrid power generation systems to the electric grid poses a unique set of challenges to utilities and system operators. This article deals with the designing methodology of an intelligent control based grid-connected a hybrid system composed of renewable energy source (RES) and storage system (SS). RES is a photovoltaic (PV) source and SS is a process of hydrogen transformation system (H2TS) which composed of alkaline water electrolysis (AWE) for decomposition water by using the PV power, a tank used for gas storage and a proton exchange membrane (PEM) fuel cell (FC) to transform the H-2 to the electrical energy. The interconnection of the grid with the power generation system (PGS) is ensured through using a DC/AC hysteresis converter and it can synchronize current with the grid voltage among an independent control of active (P) and reactive (Q) power through a possibility of the Q compensation. In the proposed system, three algorithms are applied; two used inside generation and the third is used inside the grid. Perturb and observe (P&O) maximum power point tracking (MPPT) control algorithm always finds optimal power in the PV generator. A simple cascade controls loop of DC-DC boost converter and operate the FC generator to ensure maximum power and to regulate the DC Bus voltage. In addition, adaptive fuzzy logic control (FLC) unit is developed to control the DC/AC inverter, with adopting an off-line optimization based on genetic algorithms (GAs) applauded for tune different issues as scaling factors of the FLC and PIDs gains of the PV and the H2TS control loops. Simulated results prove a big success of the proposed controls of the grid connected the hybrid PV-H2TS with good performance. (C) 2020 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.