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Incorporating direct load control demand response into active distribution system planning

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  • Moradi-Sarvestani, Sajjad
  • Jooshaki, Mohammad
  • Fotuhi-Firuzabad, Mahmud
  • Lehtonen, Matti

Abstract

Nowadays, moving toward new technologies and smart grids is a successful way of overcoming distribution system challenges. Smart grids facilitate demand-side management (DSM) which affects the expansion of network resources. In this paper, distribution system planning (DSP) considering demand response (DR), as an effective DSM tool, is presented. The proposed model is based on direct load control (DLC) DR in which a utility offers financial incentives to the customers to control their consumption through installing smart meters and switches. The utility deploys the DR program to reduce network daily peak load, which is formulated as a linear programming model, where uncertainties associated with the DR program are captured by the spherical simplex unscented transformation method. Although the DR is implemented on the basis of hourly data, integrating its high-resolution results into the DSP can cause intractability. To mitigate this issue, scenario definition and scenario reduction models using the K-means clustering algorithm are proposed. Besides, feeder reinforcement as well as renewable and conventional distributed generation (DG) installation are considered as the other network expansion alternatives. The proposed method is applied to the modified IEEE 33 Bus test system and effects of the DR program and DG installation on DSP are investigated. The results reveals that the implementation of the DLC program in the DSP either reduces or postpones feeder reinforcement actions thereby resulting in lower overall DSP costs. Furthermore, DG installation can significantly reduce operating costs, particularly purchased energy from the main grid and energy loss. Eventually, the most cost-efficient solution is obtained by incorporating both DLC and DG into the DSP.

Suggested Citation

  • Moradi-Sarvestani, Sajjad & Jooshaki, Mohammad & Fotuhi-Firuzabad, Mahmud & Lehtonen, Matti, 2023. "Incorporating direct load control demand response into active distribution system planning," Applied Energy, Elsevier, vol. 339(C).
    • Handle: RePEc:eee:appene:v:339:y:2023:i:c:s0306261923002611
    DOI: 10.1016/j.apenergy.2023.120897
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    References listed on IDEAS

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    1. Zhenghui Zhao & Joseph Mutale, 2019. "Optimal Conductor Size Selection in Distribution Networks with High Penetration of Distributed Generation Using Adaptive Genetic Algorithm," Energies, MDPI, vol. 12(11), pages 1-20, May.
    2. Siano, Pierluigi & Sarno, Debora, 2016. "Assessing the benefits of residential demand response in a real time distribution energy market," Applied Energy, Elsevier, vol. 161(C), pages 533-551.
    3. Liu, Zifa & Zhang, Zhe & Zhuo, Ranqun & Wang, Xuyang, 2019. "Optimal operation of independent regional power grid with multiple wind-solar-hydro-battery power," Applied Energy, Elsevier, vol. 235(C), pages 1541-1550.
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    Cited by:

    1. Wang, Chunling & Liu, Chunming & Chen, Jian & Zhang, Gaoyuan, 2024. "Cooperative planning of renewable energy generation and multi-timescale flexible resources in active distribution networks," Applied Energy, Elsevier, vol. 356(C).
    2. Zhichun Yang & Gang Han & Fan Yang & Yu Shen & Yu Liu & Huaidong Min & Zhiqiang Zhou & Bin Zhou & Wei Hu & Yang Lei, 2023. "A Distribution Network Planning Method Considering the Distributed Energy Resource Flexibility of Virtual Power Plants," Sustainability, MDPI, vol. 15(19), pages 1-17, September.
    3. R, Revathi & N, Senthilnathan & V, Kumar Chinnaiyan, 2024. "Hybrid optimization approach for power scheduling with PV-battery system in smart grids," Energy, Elsevier, vol. 290(C).

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