Distributed, Private, and Derandomized Allocation Algorithm for EV Charging

TL;DR

This paper presents a derandomized AIMD algorithm for efficient, privacy-preserving resource allocation among EVs, improving upon stochastic methods and handling diverse utility functions, demonstrated through simulations at a renewable-energy charging station.

Contribution

The work introduces a derandomized AIMD algorithm for resource allocation that outperforms stochastic variants and supports various utility functions, including non-monotone and Sigmoidal.

Findings

Derandomized AIMD improves allocation efficiency over stochastic versions.

Algorithm effectively handles diverse utility functions.

Simulation confirms suitability for EV charging stations.

Abstract

Efficient resource allocation is challenging when privacy of users is important. Distributed approaches have recently been used extensively to find a solution for such problems. In this work, the efficiency of distributed AIMD algorithm for allocation of subsidized goods is studied. First, a suitable utility function is assigned to each user describing the amount of satisfaction that it has from allocated resource. Then the resource allocation is defined as a total utilitarianism problem that is an optimization problem of sum of users utility functions subjected to capacity constraint. Recently, a stochastic state-dependent variant of AIMD algorithm is used for allocation of common goods among users with strictly increasing and concave utility functions. Here, the stochastic AIMD algorithm is derandomized and its efficiency is compared with the stochastic version. Moreover, the algorithm is improved to allocate subsidized goods to users with concave and non-monotone utility functions as well as users with Sigmoidal utility functions. To illustrate the effectiveness of the proposed solutions, simulation results is presented for a public renewable-energy powered charging station in which the electric vehicles (EV) compete to be recharged.