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Distributed scheduling of smart buildings to smooth power fluctuations considering load rebound

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  • Wei, Congying
  • Wu, Qiuwei
  • Xu, Jian
  • Sun, Yuanzhang
  • Jin, Xiaolong
  • Liao, Siyang
  • Yuan, Zhiyong
  • Yu, Li

Abstract

With the similar regulation characteristics as the energy storage system, the thermostatically controlled loads in smart buildings show great potential in demand response. However, its dynamic regulation characteristics may also trigger the load rebound, which misleads the power grid operator to make non-optimal regulation commands. Therefore, this paper proposes a distributed look-ahead scheduling scheme for smart buildings to smooth power fluctuations in the distribution network, where the load rebound effect is focused on. Dynamic load baselines are adopted as a link between the load regulation and load rebound. It not only reflects the impact of the load regulation on the energy consumption plans of the consumers, but also helps the power grid operator to update the regulation plan in turn. Besides, the distributed solution approach is based on the column generation where the Dantzig-Wolfe decomposition and branch-and-bound methods are combined. It adapts to the mixed integer problems and the reuse of columns can speed up both the decomposition and bounding processes. The simulation results show that, the proposed scheduling scheme can make full use of the load rebound to improve the regulation efficiency, and the solution methodology can be applied in real-time even though the decomposition algorithm is called repeatedly.

Suggested Citation

  • Wei, Congying & Wu, Qiuwei & Xu, Jian & Sun, Yuanzhang & Jin, Xiaolong & Liao, Siyang & Yuan, Zhiyong & Yu, Li, 2020. "Distributed scheduling of smart buildings to smooth power fluctuations considering load rebound," Applied Energy, Elsevier, vol. 276(C).
    • Handle: RePEc:eee:appene:v:276:y:2020:i:c:s0306261920309089
    DOI: 10.1016/j.apenergy.2020.115396
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    Cited by:

    1. Jeddi, Babak & Mishra, Yateendra & Ledwich, Gerard, 2021. "Distributed load scheduling in residential neighborhoods for coordinated operation of multiple home energy management systems," Applied Energy, Elsevier, vol. 300(C).
    2. Di Liu & Junwei Cao & Mingshuang Liu, 2022. "Joint Optimization of Energy Storage Sharing and Demand Response in Microgrid Considering Multiple Uncertainties," Energies, MDPI, vol. 15(9), pages 1-20, April.
    3. Song, Yuguang & Chen, Fangjian & Xia, Mingchao & Chen, Qifang, 2022. "The interactive dispatch strategy for thermostatically controlled loads based on the source–load collaborative evolution," Applied Energy, Elsevier, vol. 309(C).
    4. Bay, Christopher J. & Chintala, Rohit & Chinde, Venkatesh & King, Jennifer, 2022. "Distributed model predictive control for coordinated, grid-interactive buildings," Applied Energy, Elsevier, vol. 312(C).
    5. Lakshmanan, Venkatachalam & Sæle, Hanne & Degefa, Merkebu Zenebe, 2021. "Electric water heater flexibility potential and activation impact in system operator perspective – Norwegian scenario case study," Energy, Elsevier, vol. 236(C).
    6. Li, Li & Dong, Mi & Song, Dongran & Yang, Jian & Wang, Qibing, 2022. "Distributed and real-time economic dispatch strategy for an islanded microgrid with fair participation of thermostatically controlled loads," Energy, Elsevier, vol. 261(PB).
    7. Zheng, Shunlin & Qi, Qi & Sun, Yi & Ai, Xin, 2023. "Integrated demand response considering substitute effect and time-varying response characteristics under incomplete information," Applied Energy, Elsevier, vol. 333(C).
    8. Ma, Huan & Chen, Qun & Hu, Bo & Sun, Qinhan & Li, Tie & Wang, Shunjiang, 2021. "A compact model to coordinate flexibility and efficiency for decomposed scheduling of integrated energy system," Applied Energy, Elsevier, vol. 285(C).
    9. Zexu Chen & Jing Shi & Zhaofang Song & Wangwang Yang & Zitong Zhang, 2022. "Genetic Algorithm Based Temperature-Queuing Method for Aggregated IAC Load Control," Energies, MDPI, vol. 15(2), pages 1-16, January.

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