A highly efficient control framework for centralized residential charging coordination of large electric vehicle populations. (May 2020)

• Record Type: Journal Article
• Authors: Yi, Zonggen; Scoffield, Don; Smart, John; Meintz, Andrew; Jun, Myungsoo; Mohanpurkar, Manish; Medam, Anudeep
• Is Part Of: International journal of electrical power & energy systems. Volume 117(2020)
• Publication Date: 2020-05
• Subjects: Electric vehicle -- Smart charging -- Aggregator -- Centralized charging coordination -- Receding horizon control -- Highly efficient; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2019.105661
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: A highly efficient receding horizon charging coordination framework is introduced. Two-stage hierarchical optimization routine is designed to facilitate dynamic control. Charging coordination has been validated on a high-fidelity demonstration platform. Charging coordination for large PEV populations can be performed highly efficiently. Abstract: The potential for widespread adoption of plug-in electric vehicles (PEVs) brings with it the potential for negative impacts on the electric grid from electric vehicle charging. Uncoordinated PEV charging may increase electricity demand during peak hours, which could create concerns for the grid as the number of PEVs increases. Therefore, it is important to coordinate charging to alleviate potential negative impacts. Centralized charging coordination is preferred by grid operator over distributed control strategies because it can systematically allocate energy across a large population of PEVs and achieve global coordination benefits. However, centralized methods presented in the literature to date require prohibitively expensive computational resources and lack realistic PEV charging models. As a result, they cannot achieve the efficiency and accuracy required to implement charging coordination in the real world. This paper introduces a highly efficient receding horizon control framework that enables dynamic charging coordination for large PEV populations. A two-stage hierarchical optimization routine is proposed that aggregates individual PEV charging flexibility to reduce the computational complexity of the optimization process. The control framework is based on high-fidelity, validated charging system models and charging behavior models derived from a real-world data set of residential charging activities collected from thousands of charging stations over multiple years. Case studies illustrate that the proposed charging control framework is capable of effectively coordinating the charging of millions of PEVs using a standard desktop computer.