ArticlePloS one2025
Comparative techno-economic optimization of microgrid configurations using hybrid battery-hydrogen storage: NEOM case study, Saudi Arabia.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Renewable energy systems are at the core of global efforts to reduce greenhouse gas (GHG) emissions and to combat climate change. Focusing on the role of energy storage in enhancing dependability and efficiency, this paper investigates the design and optimization of a completely sustainable hybrid energy system. Furthermore, hybrid storage systems have been used to evaluate their viability and cost-benefits. Examined under a 100% renewable energy microgrid framework, three setup configurations are as follows: (1) photovoltaic (PV) and Battery Storage System (BSS), (2) Hybrid PV/Wind Turbine (WT)/BSS, and (3) Integrated PV/WT/BSS/Electrolyzer/Hydrogen Tank/Fuel Cell (FC). Using its geographical solar irradiance and wind speed data, this paper inspires on an industrial community in Neom, Saudi Arabia. HOMER software evaluates technical and economic aspects, net present cost (NPC), levelized cost of energy (COE), and operating costs. The results indicate that the PV/BSS configuration offers the most sustainable solution, with a net present cost (NPC) of $2.42M and a levelized cost of electricity (LCOE) of $0.112/kWh, achieving zero emissions. However, it has lower reliability, as validated by the provided LPSP. In contrast, the PV/WT/BSS/Elec/FC system, with a higher NPC of $2.30M and LCOE of $0.106/kWh, provides improved energy dependability. The PV/WT/BSS system, with an NPC of $2.11M and LCOE of $0.0968/kWh, offers a slightly lower cost but does not provide the same level of reliability. The surplus energy has been implemented for hydrogen production. A sensitivity analysis was performed to evaluate the impact of uncertainties in renewable resource availability and economic parameters. The results demonstrate significant variability in system performance across different scenarios.
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