Abstract

Energy prices have fluctuated significantly due to global events like the COVID-19 pandemic and geopolitical conflicts, with future projections suggesting continued volatility. This study explores how these pricing variations affect the costs and energy consumption of a smart energy management hybrid poly-generation system. For this purpose, a genetic algorithm is applied to optimize energy management under different market conditions (COVID-19, the war, the Business as Usual situation, and future price trends for 2030). The methodology also includes a sensitivity analysis, comparing Stable vs. Critical cases in Spain. The results demonstrate a 23% reduction in operational costs and an 18% decrease in energy importation under Critical conditions, while demand shifting during peak periods reduced peak electricity costs by up to 59%. These findings highlight the importance of adaptive, intelligent energy management systems for reducing costs and enhancing sustainability in volatile market conditions.