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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Acknowledgment
This paper is financially supported by National Science Foundation of China (No. 51607177, 61773252, 61673404).
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Yang, Z., Niu, Q., Guo, Y., Ma, H., Qu, B. (2018). A Fast Hybrid Meta-Heuristic Algorithm for Economic/Environment Unit Commitment with Renewables and Plug-In Electric Vehicles. In: Tan, Y., Shi, Y., Tang, Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science(), vol 10941. Springer, Cham. https://doi.org/10.1007/978-3-319-93815-8_45
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DOI: https://doi.org/10.1007/978-3-319-93815-8_45
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