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Day-ahead scheduling strategy for integrated heating and power system with high wind power penetration and integrated demand response: A hybrid stochastic/interval approach

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  • Li, Yuchun
  • Wang, Jinkuan
  • Zhang, Yan
  • Han, Yinghua

Abstract

The accommodation of wind energy is restricted due to the heat-led operation mode of CHP units. Comprehensive utilization of multi-energy coordinated supply and integration of wind energy, is deemed as an efficient solution for improving energy conservation and operational flexibility. Therefore, a novel hybrid stochastic/interval optimization for an integrated heating and power system (IHPS) day-ahead scheduling is proposed in this paper. Considering the bilateral uncertainties that exist on both sides of supply and demand, the scenario-based stochastic analyze is adopted to generate a set of possible scenarios for describing uncertainties of electrical and thermal loads. Also, the information gap decision theory (IGDT) is proposed to deal with the severe uncertainty caused by high wind power penetration. For the operation of district heating network (DHN), temperature dynamics and transmission delay are studied to exploit the heating system as a virtual storage for managing the dispatch of wind power. To further improve the system economic, the concept of price-based integrated demand response (IDR) under the background of multi-energy coupling is also introduced and the characteristics of energy substitution and load timing transfer are modeled. Simulation results show that the benefits of the proposed method for enhancing the system operation flexibility.

Suggested Citation

  • Li, Yuchun & Wang, Jinkuan & Zhang, Yan & Han, Yinghua, 2022. "Day-ahead scheduling strategy for integrated heating and power system with high wind power penetration and integrated demand response: A hybrid stochastic/interval approach," Energy, Elsevier, vol. 253(C).
    • Handle: RePEc:eee:energy:v:253:y:2022:i:c:s0360544222010921
    DOI: 10.1016/j.energy.2022.124189
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    References listed on IDEAS

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    Cited by:

    1. Wen, Lei & Song, Qianqian, 2023. "ELCC-based capacity value estimation of combined wind - storage system using IPSO algorithm," Energy, Elsevier, vol. 263(PB).
    2. Zhe Chai & Junhui Liu & Yihan Zhang & Yuge Chen & Kunming Zhang & Chang Liu & Meng Yang & Shuo Yin & Weiqiang Qiu & Zhenzhi Lin & Li Yang, 2023. "Optimal Scheduling Strategy of Regional Power System Dominated by Renewable Energy Considering Physical and Virtual Shared Energy Storage," Energies, MDPI, vol. 16(5), pages 1-20, March.

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