Cost-effective power system expansion planning under uncertainty considering fast EV charging stations with integrated energy storage support
Sun, Y.
ORCID: 0000-0002-6012-6284, Borozan, S.
ORCID: 0000-0002-2905-1050, El Samad, T.
ORCID: 0000-0003-1590-9885 , Wang, G. & Strbac, G. (2026).
Cost-effective power system expansion planning under uncertainty considering fast EV charging stations with integrated energy storage support.
Electric Power Systems Research, 259,
article number 113201.
doi: 10.1016/j.epsr.2026.113201
Abstract
The electrification of transport through electric vehicles is accelerating urban sustainability. Fast charging stations (FCS) are vital to this transition due to their high power and short turnaround times. However, scaling up electrified transport requires substantial investment in high-capacity charging infrastructure and power network reinforcement to meet charging demand. The long-term impact of FCS development on network expansion planning remains underexplored, especially under uncertainty. This paper proposes two FCS models: standalone and integrated with energy storage (ESS-FCS), to explore their strategic and economic value as non-wire investment options under uncertainty. A 40-year multi-stage stochastic planning framework is developed, leveraging scenario trees and Benders decomposition which ensure scalability and decision flexibility. Real option valuation is applied to quantify the investment value of ESS-FCS, highlighting its role in mitigating overinvestment and stranded asset risks. Case studies on modified Garver 6-bus (transmission) and IEEE 33-bus (distribution) systems reveal potential economic benefits of up to £292.06 million. Sensitivity analysis on charger ratings further demonstrates the adaptability and strategic advantage of ESS-FCS in evolving low-carbon power system landscapes.
| Publication Type: | Article |
|---|---|
| Additional Information: | © The Authors, 2026. Published by Elsevier. This is an open-access article distributed under the terms of Creative Commons: Attribution International Public License 4.0 (http://creativecommons.org/licenses/by/4.0/). |
| Publisher Keywords: | Fast EV charging stations; Energy storage system; Electric vehicles; Network expansion planning; Option value; Benders decomposition |
| Subjects: | G Geography. Anthropology. Recreation > GE Environmental Sciences H Social Sciences > HC Economic History and Conditions H Social Sciences > HD Industries. Land use. Labor H Social Sciences > HN Social history and conditions. Social problems. Social reform T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Departments: | School of Science & Technology School of Science & Technology > Department of Engineering |
| SWORD Depositor: |
Available under License Creative Commons: Attribution International Public License 4.0.
Download (3MB) | Preview
Export
Downloads
Downloads per month over past year
Metadata
Metadata