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Unit Commitment Model Considering Flexible Scheduling of Demand Response for High Wind Integration

Author

Listed:
  • Beibei Wang

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Xiaocong Liu

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China
    These authors contributed equally to this work.)

  • Feng Zhu

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China
    These authors contributed equally to this work.)

  • Xiaoqing Hu

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China
    These authors contributed equally to this work.)

  • Wenlu Ji

    (Nanjing Power Supply Company, No. 1 Aoti Street, Nanjing 210019, China
    These authors contributed equally to this work.)

  • Shengchun Yang

    (China Electric Power Research Institute, Nanjing 210096, China
    These authors contributed equally to this work.)

  • Ke Wang

    (China Electric Power Research Institute, Nanjing 210096, China
    These authors contributed equally to this work.)

  • Shuhai Feng

    (China Electric Power Research Institute, Nanjing 210096, China
    These authors contributed equally to this work.)

Abstract

In this paper, a two-stage stochastic unit commitment (UC) model considering flexible scheduling of demand response (DR) is proposed. In the proposed UC model, the DR resources can be scheduled: (1) in the first stage, as resources on a day-ahead basis to integrate the predicted wind fluctuation with lower uncertainty; (2) in the second stage, as resources on an intra-day basis to compensate for the deviation among multiple wind power scenarios considering the coupling relationship of DR on available time and capacity. Simulation results on the Pennsylvania-New Jersey-Maryland (PJM) 5-bus system and IEEE 118-bus system indicate that the proposed model can maximize the DR value with lower cost. Moreover, different types of DR resources may vary in the contract costs (capacity costs), the responsive costs (energy costs), the time of advance notice, and the minimum on-site hours. The responsive cost is considered as the most important factor affecting DR scheduling. In addition, the first-stage DR is dispatched more frequently when transmission constraints congestion occurs.

Suggested Citation

  • Beibei Wang & Xiaocong Liu & Feng Zhu & Xiaoqing Hu & Wenlu Ji & Shengchun Yang & Ke Wang & Shuhai Feng, 2015. "Unit Commitment Model Considering Flexible Scheduling of Demand Response for High Wind Integration," Energies, MDPI, vol. 8(12), pages 1-22, December.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:12:p:12390-13709:d:59814
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    References listed on IDEAS

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    1. Wei Gu & Haojun Yu & Wei Liu & Junpeng Zhu & Xiaohui Xu, 2013. "Demand Response and Economic Dispatch of Power Systems Considering Large-Scale Plug-in Hybrid Electric Vehicles/Electric Vehicles (PHEVs/EVs): A Review," Energies, MDPI, vol. 6(9), pages 1-24, August.
    2. Torres, David & Crichigno, Jorge & Padilla, Gregg & Rivera, Ruben, 2014. "Scheduling coupled photovoltaic, battery and conventional energy sources to maximize profit using linear programming," Renewable Energy, Elsevier, vol. 72(C), pages 284-290.
    3. Chinhao Chong & Weidou Ni & Linwei Ma & Pei Liu & Zheng Li, 2015. "The Use of Energy in Malaysia: Tracing Energy Flows from Primary Source to End Use," Energies, MDPI, vol. 8(4), pages 1-39, April.
    4. Falsafi, Hananeh & Zakariazadeh, Alireza & Jadid, Shahram, 2014. "The role of demand response in single and multi-objective wind-thermal generation scheduling: A stochastic programming," Energy, Elsevier, vol. 64(C), pages 853-867.
    5. Strbac, Goran, 2008. "Demand side management: Benefits and challenges," Energy Policy, Elsevier, vol. 36(12), pages 4419-4426, December.
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    Cited by:

    1. Motta, Vinicius N. & Anjos, Miguel F. & Gendreau, Michel, 2024. "Survey of optimization models for power system operation and expansion planning with demand response," European Journal of Operational Research, Elsevier, vol. 312(2), pages 401-412.
    2. Songli Fan & Qian Ai & Longjian Piao, 2018. "Hierarchical Energy Management of Microgrids including Storage and Demand Response," Energies, MDPI, vol. 11(5), pages 1-23, May.
    3. Manish Mohanpurkar & Yusheng Luo & Danny Terlip & Fernando Dias & Kevin Harrison & Joshua Eichman & Rob Hovsapian & Jennifer Kurtz, 2017. "Electrolyzers Enhancing Flexibility in Electric Grids," Energies, MDPI, vol. 10(11), pages 1-17, November.
    4. Ilias G. Marneris & Pandelis N. Biskas & Anastasios G. Bakirtzis, 2017. "Stochastic and Deterministic Unit Commitment Considering Uncertainty and Variability Reserves for High Renewable Integration," Energies, MDPI, vol. 10(1), pages 1-25, January.
    5. Jiafu Yin & Dongmei Zhao, 2018. "Fuzzy Stochastic Unit Commitment Model with Wind Power and Demand Response under Conditional Value-At-Risk Assessment," Energies, MDPI, vol. 11(2), pages 1-18, February.

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