Economic and Technical Feasibility of V2G in Non-Road Mobile Machinery sector

TL;DR

This paper explores the economic and technical potential of implementing V2G technology in the Non-Road Mobile Machinery sector, focusing on energy optimization and revenue opportunities using Bayesian methods.

Contribution

Introduces a novel Bayesian Optimization-based methodology for optimizing V2G infrastructure and operations in the NRMM sector, highlighting financial benefits.

Findings

Potential for revenue generation from V2G in NRMM

Energy cost reduction through optimized V2G strategies

Methodology demonstrates promising results despite data limitations

Abstract

This paper investigates the economic and technical feasibility of integrating Vehicle-to-Grid (V2G) technology in the Non-Road Mobile Machinery (NRMM) sector. These often-idling assets, with their substantial battery capacities, present a unique opportunity to participate in energy markets, providing grid services and generating additional revenue. A novel methodology is introduced that integrates Bayesian Optimization (BO) to optimize the energy infrastructure together with an operating strategy optimization to reduce the electricity costs while enhancing grid interaction. While the focus lies on the methodology, the financial opportunities for the use-case of an electric NRMM rental service will be presented. However, the study is limited by the availability of real-world data on the usage of electric NRMM and does not address regulatory challenges of V2G. Further research is needed to extend the model accuracy and validate these findings.