Sustainable Energy, Grids and Networks, volume 33, pages 100992

A novel renewable powered stand-alone electric vehicle parking-lot model

Publication typeJournal Article
Publication date2023-03-01
scimago Q1
wos Q1
SJR1.232
CiteScore7.9
Impact factor4.8
ISSN23524677
Electrical and Electronic Engineering
Energy Engineering and Power Technology
Renewable Energy, Sustainability and the Environment
Control and Systems Engineering
Abstract
Environmental and economic improvements prevailed by Electric Vehicles (EVs) cannot be fully achieved unless renewable energy sources partially or fully charge the EVs. However, due to the intermittent nature of renewable energy, it is challenging to rely solely on renewable energy. Previous works attempted to accurately predict renewable power generation considering the intermittent nature of temperature and wind, but adequate renewable power supply cannot always guarantee. To address this problem, we proposed a novel area-based EV parking-lot model for charge scheduling of EVs with a predefined Service Level Objective (SLO). Moreover, power demand of each area is fulfilled with distributed solar and wind generators whose probability to produce energy no less than the SLO of the parking-lot area and have predicted energy no less than that area’s power demand. The Energy Storage Supply (ESS) is incorporated to ensure sufficient power to avoid SLO violations. Deep learning technique is used to predict the probability of generating renewable power no less than the power demand of the area for each EV parking-lot area. A linear optimization problem is formulated to map distributed renewable power generators to different parking-lot areas for minimization of SLO violations, total monetary cost of energy and carbon emission, and maximize the number of charged EVs at each time interval. The evaluation on real data traces shows that for 500 EV arrived per day case, our model is effective to minimize monetary cost of power consumption by 0.88% and carbon emission by 1.89% while having very less SLO violations in a day.
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