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Semi-Markov Model of a Swarm Functioning Read chapter Read first chapter

2018 | OriginalPaper | Chapter

A Fast Hybrid Meta-Heuristic Algorithm for Economic/Environment Unit Commitment with Renewables and Plug-In Electric Vehicles

Authors : Zhile Yang, Qun Niu, Yuanjun Guo, Haiping Ma, Boyang Qu

Published in: Advances in Swarm Intelligence

Publisher: Springer International Publishing

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Abstract

To tackle with the urgent scenario of significant green house gas and air pollution emissions, it is pressing for modern power system operators to consider environmental issues in conventional economic based power system scheduling. Likewise, renewable generations and plug-in electric vehicles are both leading contributors in reducing the emission cost, however their integrations into the power grid remain to be a remarkable challenging issue. In this paper, a dual-objective economic/emission unit commitment problem is modelled considering the renewable generations and plug-in electric vehicles. A novel fast hybrid meta-heuristic algorithm is proposed combing a binary teaching-learning based optimization and the self-adaptive differential evolution for solving the proposed mix-integer problem. Numerical studies illustrate the competitive performance of the proposed method, and the economic and environmental cost have both been remarkably reduced.

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previous chapter A Hybrid Evolutionary Algorithm for Combined Road-Rail Emergency Transportation Planning
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Metadata
Title
A Fast Hybrid Meta-Heuristic Algorithm for Economic/Environment Unit Commitment with Renewables and Plug-In Electric Vehicles
Authors
Zhile Yang
Qun Niu
Yuanjun Guo
Haiping Ma
Boyang Qu
Copyright Year
2018
Book
Advances in Swarm Intelligence

Print ISBN: 978-3-319-93814-1

Electronic ISBN: 978-3-319-93815-8

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-93815-8_45

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