A Generation Expansion Planning model for integrating high shares of renewable energy: A Meta-Model Assisted Evolutionary Algorithm approach
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DOI: 10.1016/j.apenergy.2019.114085
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Cited by:
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- Seyed Hamed Jalalzad & Hossein Yektamoghadam & Rouzbeh Haghighi & Majid Dehghani & Amirhossein Nikoofard & Mahdi Khosravy & Tomonobu Senjyu, 2022. "A Game Theory Approach Using the TLBO Algorithm for Generation Expansion Planning by Applying Carbon Curtailment Policy," Energies, MDPI, vol. 15(3), pages 1-16, February.
- Zhang, Huaiyuan & Liao, Kai & Yang, Jianwei & Zheng, Shunwei & He, Zhengyou, 2024. "Frequency-constrained expansion planning for wind and photovoltaic power in wind-photovoltaic-hydro-thermal multi-power system," Applied Energy, Elsevier, vol. 356(C).
- Moradi-Sepahvand, Mojtaba & Amraee, Turaj, 2021. "Integrated expansion planning of electric energy generation, transmission, and storage for handling high shares of wind and solar power generation," Applied Energy, Elsevier, vol. 298(C).
- Zhou, Yuzhou & Zhai, Qiaozhu & Yuan, Wei & Wu, Jiang, 2021. "Capacity expansion planning for wind power and energy storage considering hourly robust transmission constrained unit commitment," Applied Energy, Elsevier, vol. 302(C).
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Keywords
Generation Expansion Planning; Unit commitment; Renewable energy technologies; Operational flexibility; Meta-model Assisted Evolutionary Algorithm; Differential evolution;All these keywords.
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