Improving robustness in strategic energy planning: a novel decision support method to deal with epistemic uncertainties

A.F. Rodríguez Matas, P. Linares, M. Pérez-Bravo, J.C. Romero

Energy Vol. 292, pp. 130463-1 - 130463-12

Summary:

This paper addresses the challenge of dealing with epistemic, i.e. non-probabilistic, uncertainties in strategic energy planning modelling. Current models have limited consideration of this type of uncertainty compared to probabilistic uncertainty, and also typically lead to overly conservative results. To address this issue, the contribution of the paper is to propose a novel decision support method which combines two decision-making methodologies into a single, internally consistent algorithm, and to show its applicability to real-size energy planning studies. Robust optimization is applied to address constraint uncertainties, while the minimax regret criterion is utilized for uncertainties in the objective function. This approach facilitates energy modelling exercises that can be more closely aligned with decision-makers' preferences for both feasibility and optimality. To demonstrate its effectiveness, the method is applied to a real-size strategic energy planning model, and the algorithm is shown to be able to provide detailed solutions in reasonable times. Ex-post evaluations confirm that this approach maintains robust optimization performance by effectively reducing the occurrence and magnitude of infeasibilities, while satisfying the minimax regret criterion across the entire range of uncertainties. Therefore, this integration preserves the distinct advantages of each methodology without any adverse effects when used together.

Spanish layman's summary:

Método de toma de decisiones para planificación energética estratégica, que aborda las incertidumbres epistémicas. Combinando optimización robusta y criterio minimax del arrepentimiento, el algoritmo ofrece soluciones robustas en tiempos razonables.

English layman's summary:

Innovative decision support method for strategic energy planning, addressing epistemic uncertainties. Combining robust optimization and minimax regret, the algorithm aligns with decision-makers' preferences, offering robust solutions in reasonable times.

Keywords: Strategic energy planning; Decision support; Uncertainty; Robustness; Robust optimization; Regret

JCR Impact Factor and WoS quartile: 9,000 - Q1 (2023)

DOI reference: https://doi.org/10.1016/j.energy.2024.130463

Published on paper: April 2024.

Published on-line: January 2024.

Citation:
A.F. Rodríguez Matas, P. Linares, M. Pérez-Bravo, J.C. Romero, Improving robustness in strategic energy planning: a novel decision support method to deal with epistemic uncertainties. Energy. Vol. 292, pp. 130463-1 - 130463-12, April 2024. [Online: January 2024]