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Energy optimization of the smart residential electrical grid considering demand management approaches

Author

Listed:
  • Zhang, Jianrui
  • Wu, Jingqun
  • Fu, Linjun
  • Wu, Qiwei
  • Huang, Yubo
  • Qiu, Wenying
  • Ali, A. Majid

Abstract

-Many researchers have investigated the optimal energy scheduling to meet the demand regarding sustainable development issues and the economic and technical indices. This study presents the day-ahead operation of a smart residential distribution electrical grid as a bi-stage-multiple-criteria decision-making modeling. The proposed energy optimization is implemented based on optimal demand management in upper-stage and multiple-criteria decision-making in lower-stage. The multiple-criteria problem is modeled from the viewpoint of the grid's operator to optimize energy consumption costs, power losses and demand side comfort. The optimal demand management in upper-stage is coordinated considering price traffic in the upstream grid. The load shifting approach and load interruption approach are presented as optimal demand management for residential consumers. The operation of demand management by using load shifting and load interruption approaches is done via deferrable loads and clippable loads in smart residential homes, respectively. The proposed energy optimization in both stages by improved sunflower optimization (IFSO) is handled, and the TOPSIS method is proposed for the best trade-off of the multiple-criteria decision-making. The day-ahead energy operation is applied on the 33-bus distribution network to investigate the effectiveness of the residential distribution electrical grid considering obtained results from mathematical modeling and demand management approaches.

Suggested Citation

  • Zhang, Jianrui & Wu, Jingqun & Fu, Linjun & Wu, Qiwei & Huang, Yubo & Qiu, Wenying & Ali, A. Majid, 2024. "Energy optimization of the smart residential electrical grid considering demand management approaches," Energy, Elsevier, vol. 300(C).
    • Handle: RePEc:eee:energy:v:300:y:2024:i:c:s0360544224014142
    DOI: 10.1016/j.energy.2024.131641
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    References listed on IDEAS

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    1. Chen Wang & Yi Wang & Kesheng Wang & Yao Dong & Yang Yang, 2017. "An Improved Hybrid Algorithm Based on Biogeography/Complex and Metropolis for Many-Objective Optimization," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-14, March.
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