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A distributed computing framework for multi-stage stochastic planning of renewable power systems with energy storage as flexibility option

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  • Flores-Quiroz, Angela
  • Strunz, Kai

Abstract

An integrated generation, transmission, and energy storage planning model accounting for short-term constraints and long-term uncertainty is proposed. The model allows to accurately quantify the value of flexibility options in renewable power systems by representing short-term operation through the unit commitment constraints. Long-term uncertainty is represented through a scenario tree. The resulting model is a large-scale multi-stage stochastic mixed-integer programming problem. To overcome the computational burden, a distributed computing framework based on the novel Column Generation and Sharing algorithm is proposed. The performance improvement of the proposed approach is demonstrated through study cases applied to the NREL 118-bus power system. The results confirm the added value of modeling short-term constraints and long-term uncertainty simultaneously. The computational case studies show that the proposed solution approach clearly outperforms the state of the art in terms of computational performance and accuracy. The proposed planning framework is used to assess the value of energy storage systems in the transition to a low-carbon power system.

Suggested Citation

  • Flores-Quiroz, Angela & Strunz, Kai, 2021. "A distributed computing framework for multi-stage stochastic planning of renewable power systems with energy storage as flexibility option," Applied Energy, Elsevier, vol. 291(C).
    • Handle: RePEc:eee:appene:v:291:y:2021:i:c:s0306261921002506
    DOI: 10.1016/j.apenergy.2021.116736
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    7. Wei, Zhinong & Yang, Li & Chen, Sheng & Ma, Zhoujun & Zang, Haixiang & Fei, Youdie, 2022. "A multi-stage planning model for transitioning to low-carbon integrated electric power and natural gas systems," Energy, Elsevier, vol. 254(PC).
    8. Ju, Chang & Ding, Tao & Jia, Wenhao & Mu, Chenggang & Zhang, Hongji & Sun, Yuge, 2023. "Two-stage robust unit commitment with the cascade hydropower stations retrofitted with pump stations," Applied Energy, Elsevier, vol. 334(C).
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    16. Renata Rodrigues Lautert & Wagner da Silva Brignol & Luciane Neves Canha & Olatunji Matthew Adeyanju & Vinícius Jacques Garcia, 2022. "A Flexible-Reliable Operation Model of Storage and Distributed Generation in a Biogas Power Plant," Energies, MDPI, vol. 15(9), pages 1-21, April.
    17. Bozhen Jiang & Qin Wang & Shengyu Wu & Yidi Wang & Gang Lu, 2024. "Advancements and Future Directions in the Application of Machine Learning to AC Optimal Power Flow: A Critical Review," Energies, MDPI, vol. 17(6), pages 1-17, March.

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