Climate-resilient energy systems planning via system-informed identification of stressful events

As the energy mix increasingly relies on weather-dependent renewable sources, energy systems become more vulnerable to climate variability and extremes. However, current planning approaches struggle to incorporate climate uncertainty in the design phase while maintaining computational tractability. We address this challenge by developing a framework that combines system-informed scenario reduction and stochastic optimization to design climate-resilient energy systems. Our method reduces data complexity by identifying representative climate scenarios that capture stress events through system response. Remarkably, five distinct patterns of multi-day energy shortages emerge across Europe, each characterized by different combinations of renewable resource availability and demand profiles. Stochastic optimization then incorporates these representative climate scenarios with their associated probabilities to design energy systems that are resilient across the full spectrum of climate variability. Results show that climate-resilient designs consistently outperform conventional single-climate designs, achieving lower costs (on average 14.8 bn EUR) for equivalent resilience levels. We identify two trade-off regions with different marginal costs of resilience: a low-resilience and a high-resilience region where marginal costs increase fivefold. Despite higher costs, trade-offs between the cost of resilience investments against energy not supplied justify pursuing the high levels of resilience. Combinations of onshore wind and hydrogen storage emerge as effective mitigation against multi-day events of energy shortage. This framework provides energy planners and policymakers with quantifiable insights into resilience investment strategies and technology selection for future climate-aware energy planning.