Ten challenges for mathematical modeling of the green-energy transition

Abstract

Abstract Purpose of Review The global transition from fossil fuels to renewable energy creates a wide array of challenges that call for new models and analytical methods. This review identifies ten mathematical modeling challenges that are central to supporting the energy transition across operational, planning, market, and policy dimensions. Our aim is to provide structured research agenda for the analytics community, focusing on areas where methodological advances can have the greatest real-world impact. Recent Findings Drawing on the expertise of leaders in the field, we present a consensus view of current modeling needs that span temporal, spatial, and institutional scales. These include short-term operational problems, long-term infrastructure planning under uncertainty, and the formulation and solution of increasingly large and complex optimization problems. In addition to technical issues, we highlight the growing importance of modeling social and behavioral dimensions such as procedural and distributive justice, retail-consumer participation, and the representation of diverse stakeholders. We identify also new challenges in market design, distributed energy integration, and the validation of large-scale models used for policy support. Summary The ten challenges reflect the breadth and complexity of the energy transition and emphasize the need for models that are scalable, robust, and socially aware. Collectively, they form a roadmap for analytics researchers aiming to contribute to the energy transition through innovative and impactful modeling.