Ten challenges for mathematical modeling of the green energy transition
File(s)
Published version
Author(s)
Type
Journal Article
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.
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.
Date Issued
2025-12-01
Date Acceptance
2025-08-20
Citation
Current Sustainable/Renewable Energy Reports, 2025, 12
ISSN
2196-3010
Publisher
Springer
Journal / Book Title
Current Sustainable/Renewable Energy Reports
Volume
12
Copyright Statement
©The Author(s) 2025. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Article Number
ARTN 26
Date Publish Online
2025-09-08