Renewable Scenario Generation Based on the Hybrid Genetic Algorithm with Variable Chromosome Length
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- Camal, S. & Teng, F. & Michiorri, A. & Kariniotakis, G. & Badesa, L., 2019. "Scenario generation of aggregated Wind, Photovoltaics and small Hydro production for power systems applications," Applied Energy, Elsevier, vol. 242(C), pages 1396-1406.
- Han, Shuo & He, Mengjiao & Zhao, Ziwen & Chen, Diyi & Xu, Beibei & Jurasz, Jakub & Liu, Fusheng & Zheng, Hongxi, 2023. "Overcoming the uncertainty and volatility of wind power: Day-ahead scheduling of hydro-wind hybrid power generation system by coordinating power regulation and frequency response flexibility," Applied Energy, Elsevier, vol. 333(C).
- Oliveira, Beatriz Brito & Carravilla, Maria Antónia & Oliveira, José Fernando, 2022. "A diversity-based genetic algorithm for scenario generation," European Journal of Operational Research, Elsevier, vol. 299(3), pages 1128-1141.
- Ekata Kaushik & Vivek Prakash & Om Prakash Mahela & Baseem Khan & Almoataz Y. Abdelaziz & Junhee Hong & Zong Woo Geem, 2022. "Optimal Placement of Renewable Energy Generators Using Grid-Oriented Genetic Algorithm for Loss Reduction and Flexibility Improvement," Energies, MDPI, vol. 15(5), pages 1-20, March.
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- Haoling Min & Pinkun He & Chunlai Li & Libin Yang & Feng Xiao, 2024. "The Temporal and Spatial Characteristics of Wind–Photovoltaic–Hydro Hybrid Power Output Based on a Cloud Model and Copula Function," Energies, MDPI, vol. 17(5), pages 1-13, February.
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Keywords
ARIMA model; copula function; genetic algorithm; renewable energy; scenario generation;All these keywords.
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