Comprehensive impact analysis of electric vehicle charging scheduling on load-duration curve. (July 2020)

• Record Type: Journal Article
• Authors: Rahman, Mohammad Mominur; Al-Ammar, Essam A.; Das, Himadry Shekhar; Ko, Wonsuk
• Is Part Of: Computers & electrical engineering. Volume 85(2020)
• Publication Date: 2020-07
• Subjects: PHEV -- V2G -- Smart charging/discharging -- Peak shaving and load leveling -- Monte-Carlo simulation -- Quadratic programming; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2020.106673
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Vehicle-to-Grid (V2G) framework permits bi-directional power flow from a vehicle's battery to the power grid. A deterministic and a probabilistic method of PHEV charging/discharging schedule are analyzed and their performances are compared based on a case study at Riyadh city, Saudi Arabia. On the deterministic method, an optimization model is proposed to reduce overall grid power consumption for utilities and operate smart charging and discharging schedule for PHEV users. The probabilistic method uses Monte Carlo Simulation (MCS) to analyze the impact and determine a coordinated Time of Unit (TOU) type for PHEV charging/ discharging schedule. ABSTRACT: Within the next decade, the number of electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) will increase exponentially owing to the environmental benefits related to their utilization. In an ideal framework condition, the vehicle-to-grid (V2G) system provides advantages such as subordinate administration of load leveling and peak shaving guidelines, and minimized upgrade costs. In this paper, deterministic and probabilistic methods of scheduling PHEV charging/discharging are analyzed, and their performances are compared based on a case study. In the case of the deterministic method, an optimization model is proposed to reduce the overall grid output power consumption for utilities and operate a smart charging/discharging schedule for PHEV users. Quadratic programming (QP) is used to solve the optimization cost function. In contrast, the probabilistic method uses Monte Carlo simulation (MCS) to analyze the impact and determine a coordinated time-of-use (TOU) structure for the PHEV charging/discharging schedule. Both methods suggest V2G feasibility for the smart grid system.