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A peak-load-reduction-based procedure to manage distribution network expansion by applying process-oriented costing of incoming components

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  • Honarmand, Mohammad Esmaeil
  • Hosseinnezhad, Vahid
  • Ghazizadeh, Mohammad Sadegh
  • Wang, Fei
  • Siano, Pierluigi

Abstract

Peak load reduction (PLR) is one of the applied strategies in demand response (DR) program to manage the costs of an electric distribution utility. Besides, this strategy can affect the costs of incoming new components (INC) from the utility viewpoint in the expansion phase, which consists of the processes of design, purchase, installation, and operation. Accordingly, considering these processes, this paper addresses a process-cost-oriented model seeing the PLR program to decide about the optimal investment value of network expansion. In the new paradigm, the costs of each process are identified, and the effect of PLR on these costs is analyzed. Moreover, to make optimal decisions, variations of the overall process costs and PLR program cost are investigated. A real case study is also provided to evaluate the capability of PLR using the proposed model. The results reveal that the overall cost is reduced by about 18%, due to the 5.7% reduction in the peak load.

Suggested Citation

  • Honarmand, Mohammad Esmaeil & Hosseinnezhad, Vahid & Ghazizadeh, Mohammad Sadegh & Wang, Fei & Siano, Pierluigi, 2019. "A peak-load-reduction-based procedure to manage distribution network expansion by applying process-oriented costing of incoming components," Energy, Elsevier, vol. 186(C).
    • Handle: RePEc:eee:energy:v:186:y:2019:i:c:s0360544219315245
    DOI: 10.1016/j.energy.2019.115852
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    Cited by:

    1. Liu, Jia & Zeng, Peter Pingliang & Xing, Hao & Li, Yalou & Wu, Qiuwei, 2020. "Hierarchical duality-based planning of transmission networks coordinating active distribution network operation," Energy, Elsevier, vol. 213(C).
    2. Ng, Rong Wang & Begam, K.M. & Rajkumar, Rajprasad Kumar & Wong, Yee Wan & Chong, Lee Wai, 2022. "A novel dynamic two-stage controller of battery energy storage system for maximum demand reductions," Energy, Elsevier, vol. 248(C).
    3. Lin, Jin & Dong, Jun & Dou, Xihao & Liu, Yao & Yang, Peiwen & Ma, Tongtao, 2022. "Psychological insights for incentive-based demand response incorporating battery energy storage systems: A two-loop Stackelberg game approach," Energy, Elsevier, vol. 239(PC).
    4. Cerna, Fernando V. & Dantas, Jamile T. & Naderi, Ehsan & Contreras, Javier, 2024. "Optimal strategy to reduce energy waste in an electricity distribution network through direct/indirect bulk load control," Energy, Elsevier, vol. 294(C).

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