Abstract

The objective of smart power systems is to combine all renewable energy sources in order to increase the electricity supply of clean energy sources. This paper proposes an optimization model for minimizing the energy cost (EC) and enhancing the power supply for rural areas by designing and analyzing three different hybrid system configurations based on integrating a biomass system with a photovoltaic (PV), wind turbine (WT) and battery system. The first hybrid system includes PV, WT, Biomass generator, and Battery storage device; the second configuration includes PV with Biomass and Battery, and the last one includes WT with Biomass and Battery. The control parameters are kept the same for both algorithms in all case studies. Real-time meteorological data are used for a remote area located in the western desert of Egypt called Abu-Monqar village. Four recent optimization algorithms, namely Slime Mould Algorithm (SMA), Seagull optimization algorithm (SOA), gray Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and Sine Cosine Algorithm (SCA) are utilized and compared with each other to ensure that all load demand is met at the lowest energy cost (EC) for the proposed hybrid system. Based on the comparison of the obtained results and the convergence curves for the three scenarios revealed that the SMA outperformed the other algorithms in terms of the best objective function. The obtained results revealed that the third scenario using SMA method provides the optimal configuration in terms of the net present cost (NPC), EC, and LPSP with 3,476,371.76$, 0.1186861 $ /kWh, and 0.032493, respectively.1)Motivations

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