CVaR Quantitative Uncertainty-Based Optimal Dispatch for Flexible Traction Power Supply System
Xiaoyu Wang, Ying Han, Luoyi Li, Jinxuan Wang, Weirong Chen, Wenjie Shen
Abstract
The flexible traction power supply system (FTPSS) integrating a photovoltaic (PV) generation system, an energy storage system (ESS), and a railway power conditioner (RPC) is a promising solution for future railways. However, the volatilities and random uncertainties of PV and traction load negatively impact the ability of FTPSS to operate safely and economically. A dispatch method combining conditional value-at-risk (CVaR) and probabilistic scenarios is proposed for FTPSS to quantify uncertainties in the risk cost. To reconcile economy and robustness, a flexible optimization dispatch model that considers the economic expense and risk cost is built. Finally, this optimization problem is formulated as a mixed integer quadratically constrained programming (MIQCP) model and solved by the GUROBI solver. Based on a genuine instance in China, the method’s validity has been demonstrated. The validity of the method is verified based on a real case in China. The essence of the proposed method is to mitigate the risks related to PV and traction load changes to some extent during the day-ahead dispatch stage by taking into account potential fluctuations in the real-time operating stage. The proposed method can help dispatch decision-makers create effective dispatch strategies and is conducive to FTPSS energy savings and risk avoidance.