This paper presents a Consensus ADMM-based modeling and solving approach for the stochastic ACOPF. The proposed optimization model considers the load forecasting uncertainty and its induced load-shedding cost via Monte Carlo sampling. The sampled scenarios are reduced using a clustering method combined with simultaneous backward reduction techniques to reduce the computational complexity. The proposed approach is tested on two IEEE systems, achieving about 2% cost reduction and more than 15 times lower reliability index in stochastic load settings compared to the baseline approach.
@article{arxiv.2411.02159,
title = {Distributed Stochastic ACOPF Based on Consensus ADMM and Scenario Reduction},
author = {Shan Yang and Yongli Zhu},
journal= {arXiv preprint arXiv:2411.02159},
year = {2024}
}
Comments
This paper has been accepted by the IEEE ICPEA 2024 conference in Taiyuan, China