Open Access

ARTICLE

Look-Ahead Vehicle-to-Grid-Aware Distribution Optimal Power Flow Approach Considering Enhanced C-ADMM-Based Electric Vehicle Aggregation Scheduling

Chao Lei¹, Yiyao Zhou^2,*, Bo Chen³

1 School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden
2 School of Electrical and Information Engineering, Xihua University, Chengdu, China
3 Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, Canada

* Corresponding Author: Yiyao Zhou. Email:

(This article belongs to the Special Issue: Operation and Control of Grid-connected New Energy and Emerging Loads)

Energy Engineering 2026, 123(11), 12 https://doi.org/10.32604/ee.2026.084834

Received 30 April 2026; Accepted 02 July 2026; Issue published 24 September 2026

Abstract

To address the impact of electric vehicle (EV) aggregation services on distribution network operation, this paper proposes a look-ahead vehicle-to-grid (V2G)-aware distribution optimal power flow (DOPF) approach that explicitly incorporates the time-varying V2G control errors arising from EV battery dispatch into real-time DOPF decisions. The V2G dispatch model is reformulated into a decomposable structure comprising one master problem and multiple subproblems, where the master problem updates the Lagrange multipliers and each subproblem optimizes the charging and discharging schedule of an individual EV. An enhanced consensus alternating direction method of multipliers algorithm is further developed by incorporating a tailored warm-start strategy to improve computational efficiency. Numerical simulations are performed on distribution networks with a fleet of EVs managed by a real-world EV aggregator. The results demonstrate that the proposed method can effectively account for time-varying V2G control errors and improve the performance of real-time DOPF operation.

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Keywords

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