IEEE Transactions on Transportation Electrification, DOI: 10.1109/TTE.2026.3740730

An ADMM-based Battery Dispatch Optimization Approach for Electric Vehicle Aggregators with Input-Discriminative Local Differential Privacy Budgets

Chao Lei¹, Qifeng Li², Farhad Shahnia³, Siqi Bu⁴, and Michael Pecht<sup>5

1</sup>School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden
²Department of Electrical and Computer Engineering, University of Central Florida, Florida, USA
³School of Engineering and Energy, Murdoch University, Murdoch, WA, Australia
⁴Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong, China
⁵Department of Mechanical Engineering, University of Maryland, Maryland, USA

For more information about this article and related research, please contact Prof. Michael Pecht.

Abstract:

Charging/discharging power data for an electric vehicle (EV) customer with a vehicle-to-grid (V2G) contract is very privacy-sensitive. To address this privacy concern, the V2G-based battery dispatch optimization (BDO) based on the alternating direction method of multipliers with input-discriminative local differential privacy (ID-LDP-based ADMM) has been developed. This ID-LDP-based ADMM algorithm involves a master problem and sub-problems, where each sub-problem represents the V2G-based battery dispatch optimization dispatch of an individual EV. Moreover, the input-discriminative local differential privacy budget of EVs can be dynamically adjusted to enhance privacy protection during critical time periods against potential attacks to the untrusted data curator in the master problem. Meanwhile, it can be proven that the optimal charge-discharge power solutions of EVs are not interfered with input-discriminative local differential privacy noise, eliminating the need for a trustworthy data curator. However, the existing DP-ADMM algorithm has to face practical challenges in balancing privacy levels, accuracy, and convergence speed within a trustworthy data curator. Case studies demonstrate that the ID-LDP-based ADMM algorithm converges to the optimal solution in about 10.6% of the time required by existing methods, even with different privacy protection levels, i.e., up to 65% larger obfuscation range than existing methods during critical time periods.

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