Author Identifier (ORCID)
Mehrdad Ghahramani: https://orcid.org/0000-0002-5926-0996
Daryoush Habibi: https://orcid.org/0000-0002-7662-6830
Asma Aziz: https://orcid.org/0000-0003-3538-0536
Abstract
The increasing penetration of renewable energy sources and the consequent rise in forecast uncertainty have underscored the need for robust operational strategies in transmission power systems. This paper introduces a risk-averse, data-driven distributionally robust optimization framework that integrates unit commitment and power flow constraints to enhance both reliability and operational security. Leveraging advanced forecasting techniques implemented via gradient boosting and enriched with cyclical and lag-based time features, the proposed methodology forecasts renewable generation and demand profiles. Uncertainty is quantified through a quantile-based analysis of forecasting residuals, which forms the basis for constructing data-driven ambiguity sets using Wasserstein balls. The framework incorporates comprehensive network constraints, power flow equations, unit commitment dynamics, and battery storage operational constraints, thereby capturing the intricacies of modern transmission systems. A worst-case net demand and renewable generation scenario is computed to further bolster the system’s risk-averse characteristics. The proposed method demonstrates the integration of data preprocessing, forecasting model training, uncertainty quantification, and robust optimization in a unified environment. Simulation results on a representative IEEE 24-bus network reveal that the proposed method effectively balances economic efficiency with risk mitigation, ensuring reliable operation under adverse conditions. This work contributes a novel, integrated approach to enhance the reliability of transmission power systems in the face of increasing uncertainty.
Document Type
Journal Article
Date of Publication
10-1-2025
Volume
18
Issue
19
Publication Title
Energies
Publisher
MDPI
School
School of Engineering
RAS ID
84293
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Comments
Ghahramani, M., Habibi, D., & Aziz, A. (2025). A risk-averse data-driven distributionally robust optimization method for transmission power systems under uncertainty. Energies, 18(19), 5245. https://doi.org/10.3390/en18195245