Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/106259
Title: Intelligent energy management algorithms for EV-charging scheduling with consideration of multiple EV charging modes
Authors: Mao, Tian
Zhang, Xin
Zhou, Baorong
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Electric Vehicle
Charging/Discharging
Issue Date: 2019
Source: Mao, T., Zhang, X., & Zhou, B. (2019). Intelligent Energy Management Algorithms for EV-charging Scheduling with Consideration of Multiple EV Charging Modes. Energies, 12(2), 265-. doi:10.3390/en12020265
Series/Report no.: Energies
Abstract: Electric vehicles (EVs) are now attracting increasing interest from both industries and countries as an environmentally friendly and energy efficient mode of travel. Therefore, the EV charging and/or discharging issue has become an important challenge and research topic in power systems in recent years. An advanced and economic EV charging process, however, should employ smart scheduling, which depends on effective and robust algorithms. To that end, a comprehensive intelligent scatter search (ISS) algorithm within the frame of a basic scatter search has been designed with both unidirectional and bidirectional charging considered. The ISS structure also supports both a flexible and constant charging power rate by respectively employing filter-SQP (sequential quadratic programming) and mixed-integer SQP as local solvers with module control. The detailed design of ISS is presented and the objectives of smoothing the daily load profile and minimizing the charging cost have been tested. Compared with methods based on GS (global search), GA (genetic algorithm), and PSO (particle swarm optimization), the outcome-verified ISS can produce attractive results with a significantly short computational time. Moreover, to handle a large scale EV charging scenario, a hybrid method comprised of a GA and ISS approach has been further developed. Simulation results also verified its prominent performance, plus superbly low computational time.
URI: https://hdl.handle.net/10356/106259
http://hdl.handle.net/10220/48906
ISSN: 1996-1073
DOI: 10.3390/en12020265
Rights: © 2019 by the Authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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