Optimal Load Scheduling of Plug-In Hybrid Electric Vehicles via Weight-Aggregation Multi-Objective Evolutionary Algorithms

In order to protect the environment and slow down global warming trend, many governments and environmentalists are keen at promoting the use of plug-in hybrid electric vehicles (PHEVs). As a result, more and more PHEVs have been put into use. However, load peak caused by their disordered charging ca...

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Vydáno v:IEEE transactions on intelligent transportation systems Ročník 18; číslo 9; s. 2557 - 2568
Hlavní autoři: Kang, Qi, Feng, ShuWei, Zhou, MengChu, Ammari, Ahmed Chiheb, Sedraoui, Khaled
Médium: Journal Article
Jazyk:angličtina
Vydáno: IEEE 01.09.2017
Témata:
Computers
Economics
Evolutionary computation
Global warming
load scheduling
multi-objective optimization
Optimization
PHEV
Plug-in hybrid electric vehicles
Power grids
PSO
Real-time systems
weight aggregation
ISSN:1524-9050, 1558-0016
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Shrnutí:In order to protect the environment and slow down global warming trend, many governments and environmentalists are keen at promoting the use of plug-in hybrid electric vehicles (PHEVs). As a result, more and more PHEVs have been put into use. However, load peak caused by their disordered charging can be detrimental to an entire power grid. Several methods have been proposed to establish ordered PHEV charging. While focusing on single-objective load scheduling, they fail to meet the real requirements that need one to conduct multiple objective optimization. This paper formulates a multi-objective load scheduling problem to minimize two competing objectives: 1) potential serious peak-to-valley difference and 2) economic loss. When we apply existing multi-objective evolutionary algorithms (MOEAs), i.e., multi-objective particle swarm optimization (MOPSO), Nondominated Sorting Genetic Algorithm II, MOEA based on decomposition, and multi-objective differential evolutionary algorithm to solve it, because its high dimension and special conditions we find that they fail to reach the Pareto Front or converge into a relatively small area only. Therefore, we propose a weight aggregation (WA) strategy and implement a novel MOEA algorithm named WA-MOPSO by incorporating WA into MOPSO to solve the problem. Its effectiveness and efficiency to generate a Pareto front of this problem are verified and compared with those of the state-of-the-art approaches. Furthermore, WA is also combined with other MOEAs to solve the defined scheduling problem.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2016.2638898