Light-duty electric vehicles to improve the integrity of the electricity grid through Vehicle-to-Grid technology: Analysis of regional net revenue and emissions savings

Authored by Mehdi Noori, Yang Zhao, Nuri C Onat, Stephanie Gardner, Omer Tatari

Date Published: 2016

DOI: 10.1016/j.apenergy.2016.01.030

Sponsors: US Department of Transportation

Platforms: AnyLogic

Model Documentation: Other Narrative

Model Code URLs: Model code not found

Abstract

Vehicle to Grid technologies utilize idle EV battery power as a grid storage tool to meet fluctuating electric power demands. Vehicle to Grid systems are promising substitutes for traditional gas turbine generators, which are relatively inefficient and have high emissions impacts. The purpose of this study is to predict the future net revenue and life cycle emissions savings of Vehicle to Grid technologies for use in ancillary (regulation) services on a regional basis in the United States. In this paper, the emissions savings and net revenue calculations are conducted with respect to five different Independent System Operator/Regional Transmission Organization regions, after which future EV market penetration rates are predicted using an Agent-Based Model designed to account for various uncertainties, including regulation service payments, regulation signal features, and battery degradation. Finally, the concept of Exploratory Modeling and Analysis is used to estimate the future net revenue and emissions savings of integrating Vehicle to Grid technology into the grid, considering the inherent uncertainties of the system. The results indicate that, for a single vehicle, the net revenue of Vehicle to Grid services is highest for the New York region, which is approximately \$42,000 per vehicle on average. However, the PJM region has an approximately \$97 million overall net revenue potential, given the 38,200 Vehicle to Grid-service available electric vehicles estimated to be on the road in the future in the PJM region, which is the highest among the studied regions. (C) 2016 Elsevier Ltd. All rights reserved.

Tags

Agent-based model Power Energy Market penetration United-states Cost Plug-in hybrid Drive vehicles Footprint Fleets