Distributed, Private, and Derandomized Allocation Algorithm for EV Charging

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 paper, the efficiency of two distributed resource allocation approaches, AIMD algorithm and competition, is studied and compared. To this end, 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. First, the stochastic AIMD algorithm is derandomized and its efficiency is compared with the stochastic version. Then, the algorithm is improved to allocate subsidized goods to users with concave and nonmonotonic utility functions as well as users with Sigmoidal utility functions. Finally, the problem is modeled as a competition game to evaluate the efficiency properties of unique equilibrium when resource allocation parameters change. 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 (EVs) compete to be recharged.

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