A novel large-scale EV charging scheduling algorithm considering V2G and reactive power management based on ADMM

Zhang, Chen; Sheinberg, Rachel; Narayana Gowda, Shashank; Sherman, Michael; Ahmadian, Amirhossein; Gadh, Rajit

doi:10.3389/fenrg.2023.1078027

A novel large-scale EV charging scheduling algorithm considering V2G and reactive power management based on ADMM

Journal Article · Tue May 09 00:00:00 EDT 2023 · Frontiers in Energy Research

DOI:https://doi.org/10.3389/fenrg.2023.1078027· OSTI ID:2424370

Zhang, Chen ^[1]; Sheinberg, Rachel ^[1]; Narayana Gowda, Shashank ^[1]; Sherman, Michael ^[1]; Ahmadian, Amirhossein ^[1]; Gadh, Rajit ^[1]

University of California, Los Angeles, CA (United States)

Electric vehicle aggregators (EVAs) that utilize vehicle-to-grid (V2G) technologies can function as both controllable loads and virtual power plants, providing key energy management services to the distribution system operator (DSO). EVAs can also balance the grid’s reactive power as a virtual static VAR compensator (SVC) and provide voltage stability by utilizing advanced electric vehicle (EV) chargers that are capable of four-quadrant operations to provide reactive power management. Finally, managed charging can benefit EVAs themselves by minimizing power factor penalties in their electricity bills. In this paper, we propose a novel EV charging scheduling algorithm based on a hierarchical distributed optimization framework that minimizes peak load and provides reactive power compensation for the DSO by collaboration with EVAs that manage both the active and the reactive charging and discharging power of participating EVs. Utilizing the alternative direction method of multipliers (ADMM), the proposed distributed optimization approach scales well with increased EV charging infrastructure by balancing active and reactive power while decreasing computational burden. In our proposed hierarchical approach, each EVA schedules the active and reactive EV charging and discharging power for 1) reactive power compensation in order to minimize power factor penalty and electricity cost accrued by the EVA, 2) satisfaction of each EV’s energy demand at minimal charging cost, and 3) peak shaving and load management for the DSO. When compared with an uncoordinated charging model, the efficacy of this proposed model is successfully demonstrated through a 300% decreased peak EV load for the DSO, 28% lower electricity costs for EV users, and 98.55% smaller power factor penalty, along with 17.58% lower overall electricity costs, for EVAs. The performance of our approach is validated in a case study with 50 EVs at multiple EVAs in an IEEE 13-bus test case and compared the results with uncoordinated EV charging.

View Accepted Manuscript (DOE)

Research Organization:: University of California, Los Angeles, CA (United States)

Sponsoring Organization:: USDOE

OSTI ID:: 2424370

Journal Information:: Frontiers in Energy Research, Journal Name: Frontiers in Energy Research Vol. 11; ISSN 2296-598X

Publisher:: Frontiers Research FoundationCopyright Statement

Country of Publication:: United States

Language:: English

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A novel large-scale EV charging scheduling algorithm considering V2G and reactive power management based on ADMM

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