Bayesian Distributionally Robust Optimization based day-ahead optimal dispatch in hybrid AC–DC distribution networks

Changhao Wu, Yinliang Xu, Hongbin Sun, Yurong Xie and Qiangyu Wen

Energy, 2025, vol. 333, issue C

Abstract: The integration of renewable energy resources, such as solar and wind power, into hybrid AC–DC distribution networks significantly transforms modern power systems, providing environmental and economic benefits. However, the inherent intermittency of these resources presents challenges, including system instability and increased operational costs. This paper introduces a Bayesian Distributionally Robust Optimization (BDRO) framework specifically designed for hybrid AC–DC networks, addressing uncertainties associated with wind turbine and photovoltaic generation. By modeling uncertainties using Bayesian principles and Kullback–Leibler divergence, the BDRO framework effectively captures underlying distributions with limited data. A Metropolis–Hastings posterior sampling method ensures computational efficiency by approximating the outer expectation.

Keywords: Renewable energy; Distribution networks; Uncertainty modeling; High renewable penetration (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:333:y:2025:i:c:s0360544225027367

DOI: 10.1016/j.energy.2025.137094

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