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Data-Driven Stochastic Scheduling for Energy Integrated Systems

Author

Listed:
  • Heng Yang

    (State Key Laboratory of Simulation and Regulation of Water Cycles in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
    These authors contributed equally to this work.)

  • Ziliang Jin

    (Department of Production Engineering, KTH Royal Institute of Technology, 114 28 Stockholm, Sweden
    These authors contributed equally to this work.)

  • Jianhua Wang

    (State Key Laboratory of Simulation and Regulation of Water Cycles in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China)

  • Yong Zhao

    (State Key Laboratory of Simulation and Regulation of Water Cycles in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China)

  • Hejia Wang

    (State Key Laboratory of Simulation and Regulation of Water Cycles in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China)

  • Weihua Xiao

    (State Key Laboratory of Simulation and Regulation of Water Cycles in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China)

Abstract

As the penetration of intermittent renewable energy increases and unexpected market behaviors continue to occur, new challenges arise for system operators to ensure cost effectiveness while maintaining system reliability under uncertainties. To systematically address these uncertainties and challenges, innovative advanced methods and approaches are needed. Motivated by these, in this paper, we consider an energy integrated system with renewable energy and pumped-storage units involved. In addition, we propose a data-driven risk-averse two-stage stochastic model that considers the features of forbidden zones and dynamic ramping rate limits. This model minimizes the total cost against the worst-case distribution in the confidence set built for an unknown distribution and constructed based on data. Our numerical experiments show how pumped-storage units contribute to the system, how inclusions of the aforementioned two features improve the reliability of the system, and how our proposed data-driven model converges to a risk-neutral model with historical data.

Suggested Citation

  • Heng Yang & Ziliang Jin & Jianhua Wang & Yong Zhao & Hejia Wang & Weihua Xiao, 2019. "Data-Driven Stochastic Scheduling for Energy Integrated Systems," Energies, MDPI, vol. 12(12), pages 1-21, June.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:12:p:2317-:d:240566
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    References listed on IDEAS

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