@inproceedings{1088c35a48c54ca3b339ca35936ae93d,
title = "A weight-aggregation multi-objective PSO algorithm for load scheduling of PHEVs",
abstract = "As the supply potential has declined gradually and the pressure for better environment intensifies, the demand for clean energy arises continuously. The consumptive demand for Plug-in Hybrid Electric Vehicle (PHEV) thus increases. 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 optimal load scheduling, they fail to meet the real requirements for efficient multiple objective optimization. This work proposes a novel weight-aggregation based multi-objective particle swarm optimization method to solve the load scheduling 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.",
keywords = "Load Scheduling, Multi-objective Optimization, PHEV, PSO, Weight Aggregation",
author = "Shuwei Feng and Qi Kang and Xiaoshuang Chen and Mengchu Zhou and Sisi Li and Jing An",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE Congress on Evolutionary Computation, CEC 2016 ; Conference date: 24-07-2016 Through 29-07-2016",
year = "2016",
month = nov,
day = "14",
doi = "10.1109/CEC.2016.7744155",
language = "English (US)",
series = "2016 IEEE Congress on Evolutionary Computation, CEC 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2896--2902",
booktitle = "2016 IEEE Congress on Evolutionary Computation, CEC 2016",
address = "United States",
}