# Hierarchical Distributed Framework for EV Charging Scheduling Using Exchange Problem

## TL;DR

This paper introduces a hierarchical distributed framework for electric vehicle charging scheduling that efficiently manages large EV populations, reduces grid impact, and outperforms existing methods in convergence speed and cost reduction.

## Contribution

It develops a novel hierarchical distributed EV charging scheduling method based on the exchange problem, improving scalability and efficiency over existing trilayer approaches.

## Key findings

- Reduces convergence time and iteration count by 60%
- Successfully scales to large EV populations of over 9000 agents
- Decreases charging costs and flattens load profiles without grid upgrades

## Abstract

In this paper, a distributed trilayer multi-agent framework is proposed for optimal electric vehicle charging scheduling (EVCS). The framework reduces the negative effects of electric vehicle charging demand on the electrical grids. To solve the scheduling problem, a novel hierarchical distributed EV charging scheduling (HDEVCS) is developed as the \textit{exchange problem}, where the agents are clustered based on their coupling constraints. According to the separability of the agents' objectives and the clusters' coupled constraints, HDEVCS is solved efficiently in a distributed manner by the alternating direction method of multipliers. Comparing to the exiting trilayer methods, HDEVCS reduces the convergence time and the iteration numbers since its structure allows the agents to update their primal optimization variable simultaneously. The performance of HDEVCS is evaluated by numerical simulation of two small- and large- scale case studies consisting of $306$ and $9051$ agents, respectively. The results verify the scalability and efficiency of the proposed method, as it reduces the convergence time and iteration numbers by $60\%$ compared to the state-of-the-art methods, flattens the load profile and decreases the charging cost considerably without violating the grid feeders' capacity. The significant outcome of our method is the accommodation of a large EV population without investment in grid expansion.