Energy management hypothesis for hybrid power system of H2/WT/PV/GMT via AI techniques

Abstract

This paper aims to attain an efficient and optimized energy management operation of Hybrid Power System (HPS) by using Artificial Intelligent (AI) controllers. The HPS comprises Wind Turbines (WTs) and Photovoltaic (PV) panels such as primary Renewable Energy Sources (RESs) in addition to both Fuel Cells (FCs) and Gas Micro–Turbines (GMTs) which are used as Backup Sources (BKUSs).To avoid the undesired negative impacts on the HPS functionality because of the RESs intermittency, the Hydrogen Storage System (HSS) is integrated into the system. Two different energy management strategies based on Neural Networks (NN) and Fuzzy Logic Control (FLC) respectively are applied to the HPS for minimizing the energy production cost and keeping the buffer role of HSS. Using MATLAB™, the proposed two AI introduced solutions are used for reaching adequate energy management operation performance for the overall HPS during 24 h load variation. From the numerical simulations, the superiority of the FLC over the NN control approach is discussed. The proposed HSS can positively act as a buffer solution to avoid drawbacks of RESs during unexpected load peaks and/or discontinuous energy production.