Cost-effective optimization of energy system integrated with hybrid battery-hydrogen storage: A techno-economic and environmental analysis for the sustainable electrification of a university campus

Abstract

Considering the growing energy demand and the pressing issue of carbon emissions, there is an urgent need to transition from conventional energy production methods to more sustainable and environmentally friendly alternatives. To enable a cost-effective transition, optimizing the energy mix at each time step is key to efficiently managing diverse energy sources. This paper proposed an Improved L & eacute;vy Flight Grey Wolf Optimization (ILFGWO) algorithm for capacity planning and energy management of a hybrid renewable energy system. The proposed algorithm is applied to minimize three performance metrics: Annualized System Cost (ASC), Levelized Cost of Energy (LCOE), and Net Present Cost (NPC). The proposed approach combines dimension learning-based hunting with multi-neighbor learning and L & eacute;vy flight distribution, which improves its exploration and exploitation capabilities. This amalgamation aids the Grey Wolf Optimization (GWO) algorithm in avoiding the stagnation problem and can verify by comparing its performance with five metaheuristic optimization algorithms. The findings reveal that the proposed algorithm outperforms other algorithms and achieves an ASC of 6574.340 (k/yr), LCOE of 0.5301 (/kWh), and NPC of 75407.213 (k). Furthermore, statistical analysis is performed to demonstrate the effectiveness of the proposed ILFGWO algorithm on 20 independent runs. The proposed technique outperforms other optimization methods by achieving the lowest minimum (6574.340 x 103 ), maximum (6576.870 x 103 ), average (6574.848 x 103 ) cost values and the smallest standard deviation (592.18 ). The paired t-test validates the best performance and reliability of the proposed approach, demonstrating that the comparisons between the proposed method and other optimization techniques are statistically significant. Regarding carbon emissions, the proposed system is expected to avoid carbon emissions by 72.05%.