Open Access
| Issue |
E3S Web Conf.
Volume 257, 2021
5th International Workshop on Advances in Energy Science and Environment Engineering (AESEE 2021)
|
|
|---|---|---|
| Article Number | 01050 | |
| Number of page(s) | 6 | |
| Section | Energy Chemistry and Energy Storage and Save Technology | |
| DOI | https://doi.org/10.1051/e3sconf/202125701050 | |
| Published online | 12 May 2021 | |
- A. B. M. Shawkat Ali, Ed., Smart Grids: Opportunities, Developments, and Trends, Green Energy and Technology. New York, NY, USA: Springer (2013) [Google Scholar]
- Z. S. Zhang, Y. Z. Sun, D. W. Gao et al., A Versatile Probability Distribution Model for Wind Power Forecast Errors and Its Application in Economic Dispatch, IEEE Transactions on Power Systems, vol. 28, no. 3, pp. 3114-3125, (2013) [Google Scholar]
- W. Zheng, W. Wu, B. Zhang et al., Fully distributed multi-area economic dispatch method for active distribution networks, IET Generation, Transmission & Distribution, vol.9, no. 12, pp. 1341-1351, (2015) [Google Scholar]
- M. Bazrafshan, and N. Gatsis, Decentralized Stochastic Optimal Power Flow in Radial Networks With Distributed Generation, IEEE Transactions on Smart Grid, vol. 8, no. 2, pp. 787-801, (2017) [Google Scholar]
- Liu Yixin, Guo Li, Wang Chengshan. Economic dispatch of microgrid based on two stage robust optimization, Proceedings of the CSEE, vol. 38, no. 14, pp. 4013-4022, (2018) [Google Scholar]
- D. Bertsimas, X. V. Doan, K. Natarajan et al., Models for minimax stochastic linear optimization problems with risk aversion, Mathematics of Operations Research, vol. 35, no. 3, pp. 580-602, (2010) [Google Scholar]
- G. Bayraksan, and D. K. Love, Data-driven stochastic programming using phi-divergences, the Operations Research Revolution, pp. 1-19: INFORMS, (2015) [Google Scholar]
- Y. Zhang, S. Shen, and J. L. Mathieu, Distributionally robust chance constrained optimal power flow with uncertain renewables and uncertain reserves provided by loads, IEEE Trans. Power Syst., vol. 32, no. 2, pp. 1378–1388, (2017) [Google Scholar]
- W. Wei, F. Liu, and S. Mei, Distributionally robust co-optimization of energy and reserve dispatch, IEEE Trans. Sustain. Energy, vol. 7, no. 1, pp. 289–300, (2016) [Google Scholar]
- P. Xiong, P. Jirutitijaroen, and C. Singh, A distributionally robust optimization model for unit commitment considering uncertain wind power generation, IEEE Trans. Power Syst., vol. 32, no. 7, pp. 39–49, (2017) [Google Scholar]
- M. Khanabadi, Y. Fu, and L. Gong, A fully parallel stochastic multiarea power system operation considering large-scale wind power integration, IEEE Trans. Sustain. Energy, vol. 9, no. 1, pp. 138–147, (2018) [Google Scholar]
- J. F. Franco, M. J. Rider, M. Lavorato, and R. Romero, Optimal conductor size selection and reconductoring in radial distribution systems using a mixed-integer LP approach, IEEE Trans. Power Syst., vol. 28, no. 1, pp. 10–20, (2013) [Google Scholar]
- M. Farivar and S. H. Low, Branch flow model: Relaxations and convexification—Part I, IEEE Trans. Power Syst., vol. 28, no. 3, pp. 2554–2564, (2013) [Google Scholar]
- T Ding, Y Qingrun, L Xiyuan, etc. Duality-Free Decomposition Based Data-Driven Stochastic Security-Constrained Unit Commitment, IEEE Trans. On Sustainable Energy, vol. 10, no. 1, pp. 2825–2836, (2019) [Google Scholar]
- B. Zeng and L. Zhao, Solving two-stage robust optimization problems using a column-andconstraint generation method, Oper. Res. Lett., vol. 41, no. 5, pp. 457–461, (2013) [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.