Multi-objective unit commitment optimization with ultra-low emissions under stochastic and fuzzy uncertainties-Reference-Cited by-同舟云学术

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Multi-objective unit commitment optimization with ultra-low emissions under stochastic and fuzzy uncertainties

Published:2020-03-06 Issue:1 Volume:12 Page:1-15
ISSN:1868-8071
Container-title:International Journal of Machine Learning and Cybernetics
language:en
Short-container-title:Int. J. Mach. Learn. & Cyber.

Author:

Li You,Li Huaxiong,Wang Bo^ORCID,Zhou Min,Jin Mei

Funder

National Natural Science Foundation of China

Natural Science Foundation of Jiangsu Province

Fundamental Research Funds for the Central Universities

Young Scholar Support Programme of Nanjing University of Finance & Economics

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

http://link.springer.com/content/pdf/10.1007/s13042-020-01103-9.pdf

Reference47 articles.

1. Wang Z, Zhang L, Li G, Yang L (2018) Active power and reactive power dispatch of wind farm based on wavelet learning. Int J Mach Learn Cybern 9(2):217–223

2. Zhao Y, Xing W, Lu W, Zhang X, Christensen TH (2012) Environmental impact assessment of the incineration of municipal solid waste with auxiliary coal in China. Waste Manag 32(10):1989–1998

3. Zheng QP, Wang J, Liu AL (2015) Stochastic optimization for unit commitment-a review. IEEE Trans Power Syst 30(4):1913–1924

4. Gooi H, Mendes D, Bell KRW, Kirschen D (1999) Optimal scheduling of spinning reserve. IEEE Trans Power Syst 14(4):1485–1492

5. Wang S, Pedrycz W (2015) Robust granular optimization: a structured approach for optimization under integrated uncertainty. IEEE Trans Fuzzy Syst 23(5):1372–1386

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4. Distributionally Robust Proximal Policy Optimization for Unit Commitment with Wind Power Uncertainty;2022 First International Conference on Cyber-Energy Systems and Intelligent Energy (ICCSIE);2023-01-14

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