EOS: Impact Evaluation of Electric Vehicle Adoption on Peak Load Shaving Using Agent-Based Modeling

William J. Howell, Ziqian Dong () and Roberto Rojas-Cessa ()
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William J. Howell: Department of Electrical and Computer Engineering, New York Institute of Technology, New York, NY 10023, USA
Ziqian Dong: Department of Electrical and Computer Engineering, New York Institute of Technology, New York, NY 10023, USA
Roberto Rojas-Cessa: Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA

Energies, 2024, vol. 17, issue 20, 1-14

Abstract: The increasing adoption of electric vehicles (EVs) by the general population creates an opportunity to deploy the energy storage capability of EVs for performing peak energy shaving in their households and ultimately in their neighborhood grid during surging demand. However, the impact of the adoption rate in a neighborhood might be counterbalanced by the energy demand of EVs during off-peak hours. Therefore, achieving optimal peak energy shaving is a product of a sensitive balancing process that depends on the EV adoption rate. In this paper, we propose EOS, an agent-based simulation model, to represent independent household energy usage and estimate the real-time neighborhood energy consumption and peak shaving energy amount of a neighborhood. This study uses Residential Energy Consumption Survey (RECS) and the American Time Use Survey (ATUS) data to model realistic real-time household energy use. We evaluate the impact of the EV adoption rates of a neighborhood on performing energy peak shaving during sudden energy surges. Our findings reveal these trade-offs and, specifically, a reduction of up to 30% of the peak neighborhood energy usage for the optimal neighborhood EV adoption rate in a 1089 household neighborhood.

Keywords: agent-based model; electric vehicles; load-peak shaving; neighborhood energy demand; vehicle-to-grid services (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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