Profit-aware Online Vehicle-to-Grid Decentralized Scheduling under Multiple Charging Stations

TL;DR

This paper introduces a profit-maximizing, decentralized online scheduling algorithm for vehicle-to-grid systems with multiple charging stations, optimizing profits and load balancing in large-scale PEV charging networks.

Contribution

It presents a novel online decentralized greedy algorithm for V2G scheduling that maximizes profit and efficiently guides vehicles to charging stations in large-scale settings.

Findings

The proposed algorithm outperforms alternative strategies in profit and load flatness.

Simulation results identify an optimal number of charging stations for maximum profit.

The approach effectively balances demand and supply in V2G systems.

Abstract

Fluctuations in electricity tariffs induced by the sporadic nature of demand loads on power grids has initiated immense efforts to find optimal scheduling solutions for charging and discharging plug-in electric vehicles (PEVs) subject to different objective sets. In this paper, we consider vehicle-to-grid (V2G) scheduling at a geographically large scale in which PEVs have the flexibility of charging/discharging at multiple smart stations coordinated by individual aggregators. We first formulate the objective of maximizing the overall profit of both, demand and supply entities, by defining a weighting parameter. We then propose an online decentralized greedy algorithm for the formulated mixed integer non-linear programming (MINLP) problem, which incorporates efficient heuristics to practically guide each incoming vehicle to the most appropriate charging station (CS). The better performance of the presented algorithm compared to an alternative allocation strategy is demonstrated through simulations in terms of the overall achievable profit and flatness of the final electricity load. Moreover, the results of simulations reveal the existence of optimal number of deployed stations at which the overall profit can be maximized.