Impact of Stochastic Generation/Load Variations on Distributed Optimal Energy Management in DC Microgrids for Transportation Electrification

作     者：Xie, Siyu Nazari, Masoud H. Wang, Le Yi Yin, George Chen, Wen 

作者机构：Wayne State Univ Dept Elect & Comp Engn Detroit MI 48202 USA Univ Connecticut Dept Math Storrs CT 06269 USA Wayne State Univ Div Engn Technol Detroit MI 48202 USA 

出 版 物：《IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS》 (IEEE Trans. Intell. Transp. Syst.)

年 卷 期：2022年第23卷第7期

页      面：7196-7205页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0814[工学-土木工程] 0823[工学-交通运输工程] 

基　　金：Air Force Office of Scientific Research (AFOSR) [FA9550-18-1-0268] 

主　　题：DC microgrids optimal load distribution load variations distributed algorithms stochastic analysis 

摘      要：This paper studies the impact of stochastic load variations on distributed optimal load tracking and allocation (OLTA) problems in cyber-physical DC microgrids (NIGs) for transportation electrification. Without load variations, the distributed optimization strategies developed in our earlier work can achieve convergence to global optimal solutions in a multiobjective optimization that balances fair load allocation and power loss reduction. Under persistent stochastic load variations, this paper develops distributed optimal strategies to track time-varying loads under noisy observations and establishes their convergence properties and error bounds. The limiting behavior of the errors characterizes the fundamental impact of the step size on irreducible errors due to conflict between attenuating observation noises and tracking load changes. Optimality conditions and algorithms for selecting the optimal step size are introduced to guide step size selection in practical applications. Simulation studies on real-world systems demonstrate the effectiveness of the proposed algorithms and validate the theoretical results.