充电站
盈利能力指数
利润(经济学)
电动汽车
运输工程
计算机科学
峰值需求
模拟
汽车工程
电气工程
电
工程类
功率(物理)
业务
财务
经济
微观经济学
物理
量子力学
作者
Yantao Huang,Kara M. Kockelman
标识
DOI:10.1016/j.trd.2019.11.008
摘要
Battery-only electric vehicles (BEVs) generally offer better air quality through lowered emissions, along with energy savings and security. The issue of long-duration battery charging makes charging-station placement and design key for BEV adoption rates. This work uses genetic algorithms to identify profit-maximizing station placement and design details, with applications that reflect the costs of installing, operating, and maintaining service equipment, including land acquisition. Fast electric vehicle charging stations (EVCSs) are placed across a congested city's network subject to stochastic demand for charging under a user-equilibrium traffic assignment. BEV users’ station choices consider endogenously determined travel times and on-site charging queues. The model allows for congested-travel and congested-station feedback into travelers’ route choices under elastic demand and BEV owners’ station choices, as well as charging price elasticity for BEV charging users. Boston-network results suggest that EVCSs should locate mostly along major highways, which may be a common finding for other metro settings. If 10% of current EV owners seek to charge en route, a user fee of $6 for a 30-min charging session is not enough for station profitability under a 5-year time horizon in this region. However, $10 per BEV charging delivers a 5-year profit of $0.82 million, and 11 cords across 3 stations are enough to accommodate a near-term charging demand in this Boston-area application. Shorter charging sessions, higher fees, and/or allowing for more cords per site also increase profits generally, everything else constant. Power-grid and station upgrades should keep pace with demand, to maximize profits over time, and avoid on-site congestion.
科研通智能强力驱动
Strongly Powered by AbleSci AI