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Scenario-Based Distributionally Robust Unit Commitment Optimization Involving Cooperative Interaction with Robots

Author

Listed:
  • Xuanning Song

    (Nanjing University)

  • Bo Wang

    (Nanjing University)

  • Pei-Chun Lin

    (Feng Chia University)

  • Guangyu Ge

    (Jiangsu Second Normal University)

  • Ran Yuan

    (Nanjing University)

  • Junzo Watada

    (Waseda University)

Abstract

With the increasing penetration of renewable energy, uncertainty has become the main challenge of power systems operation. Fortunately, system operators could deal with the uncertainty by adopting stochastic optimization (SO), robust optimization (RO) and distributionally robust optimization (DRO). However, choosing a good decision takes much experience, which can be difficult when system operators are inexperienced or there are staff shortages. In this paper, a decision-making approach containing robotic assistance is proposed. First, advanced clustering and reduction methods are used to obtain the scenarios of renewable generation, thus constructing a scenario-based ambiguity set of distributionally robust unit commitment (DR-UC). Second, a DR-UC model is built according to the above time-series ambiguity set, which is solved by a hybrid algorithm containing improved particle swarm optimization (IPSO) and mathematical solver. Third, the above model and solution algorithm are imported into robots that assist in decision making. Finally, the validity of this research is demonstrated by a series of experiments on two IEEE test systems.

Suggested Citation

  • Xuanning Song & Bo Wang & Pei-Chun Lin & Guangyu Ge & Ran Yuan & Junzo Watada, 2024. "Scenario-Based Distributionally Robust Unit Commitment Optimization Involving Cooperative Interaction with Robots," Information Systems Frontiers, Springer, vol. 26(1), pages 9-23, February.
    • Handle: RePEc:spr:infosf:v:26:y:2024:i:1:d:10.1007_s10796-022-10335-9
    DOI: 10.1007/s10796-022-10335-9
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    References listed on IDEAS

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    1. Yuan, Ran & Wang, Bo & Mao, Zhixin & Watada, Junzo, 2021. "Multi-objective wind power scenario forecasting based on PG-GAN," Energy, Elsevier, vol. 226(C).
    2. Zhang, Yachao & Liu, Yan & Shu, Shengwen & Zheng, Feng & Huang, Zhanghao, 2021. "A data-driven distributionally robust optimization model for multi-energy coupled system considering the temporal-spatial correlation and distribution uncertainty of renewable energy sources," Energy, Elsevier, vol. 216(C).
    3. Francesco Piccialli & Vincenzo Schiano Cola & Fabio Giampaolo & Salvatore Cuomo, 2021. "The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic," Information Systems Frontiers, Springer, vol. 23(6), pages 1467-1497, December.
    4. Luo, Xing & Zhu, Xu & Lim, Eng Gee, 2019. "A parametric bootstrap algorithm for cluster number determination of load pattern categorization," Energy, Elsevier, vol. 180(C), pages 50-60.
    5. Liang, Weikun & Lin, Shunjiang & Lei, Shunbo & Xie, Yuquan & Tang, Zhiqiang & Liu, Mingbo, 2022. "Distributionally robust optimal dispatch of CCHP campus microgrids considering the time-delay of pipelines and the uncertainty of renewable energy," Energy, Elsevier, vol. 239(PC).
    6. Sayed, Ahmed Rabee & Wang, Cheng & Chen, Sheng & Shang, Ce & Bi, Tianshu, 2021. "Distributionally robust day-ahead operation of power systems with two-stage gas contracting," Energy, Elsevier, vol. 231(C).
    7. Weidong Zhao & Qingfeng Zeng & Guangjian Zheng & Liu Yang, 2017. "The resource allocation model for multi-process instances based on particle swarm optimization," Information Systems Frontiers, Springer, vol. 19(5), pages 1057-1066, October.
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    Cited by:

    1. Lionel P. Robert & Marcelo Fantinato & Sangseok You & Patrick C. K. Hung, 2024. "Social Robotics Business and Computing," Information Systems Frontiers, Springer, vol. 26(1), pages 1-8, February.

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