A Distributionally Robust Model Predictive Control for Static and Dynamic Uncertainties in Smart Grids
<|reference_start|>A Distributionally Robust Model Predictive Control for Static and Dynamic Uncertainties in Smart Grids: The integration of various power sources, including renewables and electric vehicles, into smart grids is expanding, introducing uncertainties that can result in issues like voltage imbalances, load fluctuations, and power losses. These challenges negatively impact the reliability and stability of online scheduling in smart grids. Existing research often addresses uncertainties affecting current states but overlooks those that impact future states, such as the unpredictable charging patterns of electric vehicles. To distinguish between these, we term them static uncertainties and dynamic uncertainties, respectively. This paper introduces WDR-MPC, a novel approach that stands for two-stage Wasserstein-based Distributionally Robust (WDR) optimization within a Model Predictive Control (MPC) framework, aimed at effectively managing both types of uncertainties in smart grids. The dynamic uncertainties are first reformulated into ambiguity tubes and then the distributionally robust bounds of both dynamic and static uncertainties can be established using WDR optimization. By employing ambiguity tubes and WDR optimization, the stochastic MPC system is converted into a nominal one. Moreover, we develop a convex reformulation method to speed up WDR computation during the two-stage optimization. The distinctive contribution of this paper lies in its holistic approach to both static and dynamic uncertainties in smart grids. Comprehensive experiment results on IEEE 38-bus and 94-bus systems reveal the method's superior performance and the potential to enhance grid stability and reliability.<|reference_end|>
@article{li2024a,
    title={A Distributionally Robust Model Predictive Control for Static and
  Dynamic Uncertainties in Smart Grids},
    author={Qi Li, Ye Shi, Yuning Jiang, Yuanming Shi, Haoyu Wang, H.Vincent Poor},
    journal={arXiv preprint arXiv:2403.16402},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.16402},
    primaryClass={eess.SY cs.SY}
}