Optimization and sizing of isolated hybrid solar PV/DG/battery energy systems for residential community in hot climate areas of Saudi Arabia

This chapter focuses on the development of a smart power system that integrates renewable energy resources to optimize the supply of clean electricity, particularly for rural areas in hot climates. The main objective is to reduce energy production costs (EC) while enhancing the reliability and efficiency of power delivery in remote rural areas. To achieve this, this chapter explores two advanced optimization techniques, namely, the hippopotamus optimization (HO) and the greylag goose optimization (GGO) techniques. These techniques are applied to a hybrid energy system configuration that combines solar photovoltaic (PV) panels, diesel generators (DG), and battery energy storage to meet energy demands with increased stability and efficiency. Through comparative analysis, this study reveals that the HO algorithm demonstrates greater efficiency than the GGO, showing a shorter runtime and more robust convergence characteristics. Simulation results indicate that the HO algorithm achieves a superior best fitness value of 0.0963 compared to GGO's 0.1129, highlighting HO's better convergence behavior and efficiency. Additionally, the HO-optimized system shows a net present cost (NPC) of 21.3 M$ and a cost of energy (COE) of 0.33 $/kWh, which are significantly lower than GGO's 27.2 M$ NPC and 0.42 $/kWh COE. In terms of energy reliability, GGO achieved a lower loss of power supply probability (LPSP) of 0.022 compared to HO's 0.0367, indicating its strength in maintaining a continuous power supply. Meanwhile, the HO approach reduces the excess energy directed to the dummy load to 0.0398, offering improved economic efficiency. These results underscore the potential for the proposed hybrid PV/DG/battery system to deliver cost-effective, stable energy for offgrid communities, offering sustainable energy solutions with minimal reliance on conventional power sources. Furthermore, this chapter provides insights into optimization strategies that can be applied in smart power systems to facilitate sustainable energy solutions in off-grid and rural regions.