Work

Adaptive two-stage stochastic energy infrastructure expansion planning

Year: 2026

Type: article

Abstract: This paper introduces an adaptive two-stage stochastic optimization model for energy infrastructure expansion planning under demand uncertainty. Unlike traditional two-stage models, which can be too rigid, or multi-stage models, which can be overly flexible, our model seeks a balance between commitment and flexibility. It allows each investment decision to adapt at one or two designated adaptation times to the unfolding uncertainty while maintaining a static policy before and after these times, where the adaptation times are determined within the proposed optimization model. The model’s performance is evaluated using two metrics and compared with conventional approaches. To enhance the model’s practicality, five strategies are presented for sharing adaptation times among related investments, further reducing the number of plan revisions. A case study on Rwanda’s electrification plan demonstrates that this approach can save up to 3.44% compared to the two-stage model, while requiring significantly fewer adaptations than the multi-stage model. Additionally, the model also provides actionable guidance for policy makers including the optimal adaptation frequency and timing of policy revisions and the ideal planning horizon length.

Source: International Journal of Electrical Power & Energy Systems

DOI: 10.1016/j.ijepes.2026.111806

Authors: Yuang Chen, Zhewei Li, Beste Basciftci, Valerie M. Thomas

Institutions: Chinese University of Hong Kong, Shenzhen, Tianjin University, University of Iowa, Georgia Institute of Technology

Language: English

Topic: Electric Power System Optimization

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy, Climate action

Open Access status: gold

Estimated APC paid: $3,920