Microgrid Revenue Maximization by Charging Scheduling of EVs in Multiple Parking Stations

TL;DR

This paper presents a polynomial-time approximation algorithm for EV charging scheduling in microgrids with multiple stations, aiming to maximize revenue while minimizing peak demand and respecting constraints.

Contribution

It introduces a near-optimal, valley-filling scheduling algorithm for microgrid EV charging that handles multiple stations and global peak constraints, a problem not previously addressed.

Findings

Achieves 98% of the optimal revenue in simulations.

Reduces peak demand by 16% compared to existing algorithms.

Improves resource utilization in EV charging scheduling.

Abstract

Nowadays, there has been a rapid growth in global usage of the electronic vehicles (EV). Despite apparent environmental and economic advantages of EVs, their high demand charging jobs pose an immense challenge to the existing electricity grid infrastructure. In microgrids, as the small-scale version of traditional power grid, however, the EV charging scheduling is more challenging. This is because, the microgrid owner, as a large electricity customer, is interested in shaving its global peak demand, i.e., the aggregated demand over multiple parking stations, to reduce total electricity cost. While the EV charging scheduling problem in single station scenario has been studied extensively in the previous research, the microgrid-level problem with multiple stations subject to a global peak constraint is not tackled. This paper aims to propose a near-optimal EV charging scheduling mechanism in a microgrid governed by a single utility provider with multiple charging stations. The goal is to maximize the total revenue while respecting both local and global peak constraints. The underlying problem, however, is a NP-hard mixed integer linear problem which is difficult to tackle and calls for approximation algorithm design. We design a primaldual scheduling algorithm which runs in polynomial time and achieves bounded approximation ratio. Moreover, the proposed global scheduling algorithm applies a valley-filling strategy to further reduce the global peak. Simulation results show that the performance of the proposed algorithm is 98% of the optimum, which is much better than the theoretical bound obtained by our approximation analysis. Our algorithm reduces the peak demand obtained by the existing alternative algorithm by 16% and simultaneously achieves better resource utilization.