2016
DOI: 10.3390/en9030186
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Electric Vehicle Charging and Discharging Coordination on Distribution Network Using Multi-Objective Particle Swarm Optimization and Fuzzy Decision Making

Abstract: This paper proposed a optimal strategy for coordinated operation of electric vehicles (EVs) charging and discharging with wind-thermal system. By aggregating a large number of EVs, the huge total battery capacity is sufficient to stabilize the disturbance of the transmission grid. Hence, a dynamic environmental dispatch model which coordinates a cluster of charging and discharging controllable EV units with wind farms and thermal plants is proposed. A multi-objective particle swarm optimization (MOPSO) algorit… Show more

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Cited by 37 publications
(22 citation statements)
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“…The MOPSO algorithm is proposed by Coello et al [29] in 2004 to solve the multi-objective optimization problem and has been widely researched and applied [30][31][32]. Here, the MOPSO is employed because of its high searching ability and low time complexity [29].…”
Section: Multi-object Optimizationmentioning
confidence: 99%
“…The MOPSO algorithm is proposed by Coello et al [29] in 2004 to solve the multi-objective optimization problem and has been widely researched and applied [30][31][32]. Here, the MOPSO is employed because of its high searching ability and low time complexity [29].…”
Section: Multi-object Optimizationmentioning
confidence: 99%
“…Traditional control theory is powerful in a linear system, but is difficult to control in a lithium battery, which is a nonlinear system [32,33]. FLC can simulate human knowledge and reasoning and is robust even when measurements are imprecise or when input characteristics change [34][35][36].…”
Section: Fuzzy Logic Control Strategymentioning
confidence: 99%
“…A study of the optimal charging strategy for lithium-ion batteries was also presented in [32]. Another work [33] presented the coordination of PEV charging and discharging using particle swarm optimisation and fuzzy decision making. However, the advantage of MPC lies in its ability to incorporate predictions and constraints in the optimisation process.…”
Section: Introductionmentioning
confidence: 99%