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Data-Driven Distributionally Robust Scheduling of Community Comprehensive Energy Systems Considering Integrated Load Control

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Flexible Load Control for Enhancing Renewable Power System Operation

Part of the book series: Power Systems ((POWSYS))

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Abstract

Recently, the depletion of fossil fuels and escalating environmental pollution have emerged as primary challenges confronting human civilization. The advent and progression of renewable generations (RGs) present a viable solution to such issues (Potrč et al. in Renew Sustain Energy Rev 146:111186, 2021). A prominent illustration of this solution and an integral contributor to the evolution of the energy internet is the Community Integrated Energy System (CIES) (Li et al. in J Clean Prod 378:134540, 2022).

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Authors and Affiliations

  1. School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, Hubei, China

    Yuanzheng Li

  2. School of Electrical Engineering, Northeast Electric Power University, Jilin, China

    Yang Li

  3. School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, Hubei, China

    Zhigang Zeng

Authors
  1. Yuanzheng Li
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  2. Yang Li
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  3. Zhigang Zeng
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Correspondence to Yuanzheng Li .

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Li, Y., Li, Y., Zeng, Z. (2024). Data-Driven Distributionally Robust Scheduling of Community Comprehensive Energy Systems Considering Integrated Load Control. In: Flexible Load Control for Enhancing Renewable Power System Operation. Power Systems. Springer, Singapore. https://doi.org/10.1007/978-981-97-0312-8_10

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  • DOI: https://doi.org/10.1007/978-981-97-0312-8_10

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  • Online ISBN: 978-981-97-0312-8

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