A novel renewable powered stand-alone electric vehicle parking-lot model. (March 2023)

• Record Type: Journal Article
• Authors: Jawad, Muhammad; Asghar, Hira; Arshad, Jehangir; Javed, Abbas; Qureshi, Muhammad Bilal; Ali, Sahibzada Muhammad; Shabbir, Noman; Rassõlkin, Anton
• Is Part Of: Sustainable energy, grids and networks. Volume 33(2023)
• Publication Date: 2023-03
• Subjects: Demand response -- Electric vehicles -- Forecasting -- Linear programming -- Optimization -- Smart grids; Renewable energy sources -- Periodicals; Smart power grids -- Periodicals; Electric power systems -- Periodicals; 333.794
• DOI: 10.1016/j.segan.2022.100992
• Languages: English
• ISSNs: 2352-4677

Abstract

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. Highlights: A novel stand-alone electric vehicle (EV) parking-lot energy management system. Efficient allocation of renewable generators based on predicted tail distribution. A novel area-based EV charge scheduling system for Smart EV parking-lots. Service Level Objective based EV charge management system design. Linear programming based optimization of energy consumption cost and carbon emission.