Decentralized Electric Vehicle Charging Control via a Novel Shrunken Primal Multi-Dual Subgradient (SPMDS) Algorithm

TL;DR

This paper introduces a scalable decentralized EV charging control method using a novel SPMDS algorithm, reducing computational costs significantly while coordinating large EV fleets without communication.

Contribution

It proposes a dimension reduction approach and a decentralized SPMDS algorithm for scalable EV charging control, overcoming existing limitations in network size and EV numbers.

Findings

Reduces over 43% of primal computational cost.

Reduces up to 68% of dual computational cost.

Effective in large-scale EV charging scenarios.

Abstract

The charging processes of a large number of electric vehicles (EVs) require coordination and control for the alleviation of their impacts on the distribution network and for the provision of various grid services. However, the scalability of existing EV charging control paradigms are limited by either the number of EVs or the distribution network dimension, largely impairing EVs' aggregate service capability and applicability. To overcome the scalability barrier, this paper, motivated by the optimal scheduling problem for the valley-filling service, (1) proposes a novel dimension reduction methodology by grouping EVs (primal decision variables) and establishing voltage (global coupled constraints) updating subsets for each EV group in the distribution network and (2) develops a novel decentralized shrunken primal multi-dual subgradient (SPMDS) optimization algorithm to solve this reduced-dimension problem. The proposed SPMDS-based control framework requires no communication between EVs, reduces over 43% of the computational cost in the primal subgradient update, and reduces up to 68% of the computational cost in the dual subgradient update. The efficiency and efficacy of the proposed algorithm are demonstrated through simulations over a modified IEEE 13-bus test feeder and a modified IEEE 123-bus test feeder.