Predictive Management of Electric Vehicles in a Community Microgrid

TL;DR

This paper presents a predictive EV management strategy in a community microgrid that reduces energy costs and load ramping by accounting for driver behavior uncertainties using real-world data and a two-stage optimization model.

Contribution

It introduces a novel predictive control approach that incorporates driver behavior uncertainties into microgrid EV management, improving cost efficiency and load stability.

Findings

Energy cost reduced significantly

Load ramping decreased by up to 56.3%

Effective decentralized control strategy

Abstract

The charging load from Electric vehicles (EVs) is modeled as deferrable load, meaning that the power consumption can be shifted to different time windows to achieve various grid objectives. In local community scenarios, EVs are considered as controllable storage devices in a global optimization problem together with other microgrid components, such as the building load, renewable generations, and battery energy storage system, etc. However, the uncertainties in the driver behaviors have tremendous impact on the cost effectiveness of microgrid operations, which has not been fully explored in previous literature. In this paper, we propose a predictive EV management strategy in a community microgrid, and evaluate it using real-world datasets of system baseload, solar generation and EV charging behaviors. A two-stage operation model is established for cost-effective EV management, i.e. wholesale market participation in the first stage and load profile following in the second stage. Predictive control strategies, including receding horizon control, are adapted to solve the energy allocation problem in a decentralized fashion. The experimental results indicate the proposed approach can considerably reduce the total energy cost and decrease the ramping index of total system load up to 56.3%.