Modeling disruptive events in renewable energy supply: A review

The accelerating shift toward renewable energy necessitates robust planning frameworks that can accommodate unexpected disruptions. While various energy system modeling methods are widely used for planning and decision-making, they each have their own strengths and weaknesses in capturing uncertainty in the outcomes of disruptive event modeling. This review addresses a critical research gap by systematically analyzing how such methods quantify and mitigate the impact of disruptive events on renewable energy supply. It is the first to comprehensively assess modeling approaches specifically in this context. The study categorizes 108 disruptive events from 102 articles into four primary types: natural (e.g., floods, heatwaves), human-caused intentional (e.g., technological innovations), socio-political (e.g., wars, policy changes), and economic (e.g., interest rate shifts, carbon tax changes). Articles were selected using a PRISMA-compliant methodology from multiple sources, applying strict inclusion criteria: relevance to renewable energy, a clear focus on disruptive events, and use of modeling methods. Findings confirm the hypothesis that incorporating broader socio-economic and environmental criteria into modeling improves the robustness and realism of planning under disruptive conditions. The review shows that relying on one modeling objective such as cost often limits the ability to capture uncertainty and stakeholder concerns. Instead, models that integrate multiple criteria and generate a range of feasible solutions offer more resilient and adaptable planning outcomes. The study recommends combining complementary modeling strategies and tailoring criteria to stakeholder priorities. Such combined modeling approaches are well suited to future studies, enabling flexible, risk-informed, and context-sensitive modeling of disruptive events in renewable energy supply systems.