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Interactive Time-of-use demand response for industrial electricity customers: A case study

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  • Kholerdi, Somayeh Siahchehre
  • Ghasemi-Marzbali, Ali

Abstract

Demand response (DR) programs are regarded as one of the most reliable and reasonable methods to benefit electricity suppliers and consumers. This paper presents a modified approach to DR based on an Interactive Time-of-Use (ITOU) model by which volunteer industrial customers and their electricity suppliers obtain the best possible performance. Owing to this aim, when studying the region's electrical load profile to determine the peak-hours, the one-year production and sales profile of industrial customers is also studied to select off-peak hours for industrial subscribers. The results of program implementation for the selected and volunteer industrial customers at the sub-transmission level are presented to evaluate the performance of the proposed model. Considering the electricity bills of the subscribers in the program based on reduced energy consumption at peak hours (selected by the utility), and increased energy consumption at the off-peak price (selected by industrial customers), the economic benefits to industrial customers are calculated and verified. Plotting new load curves confirms load shifting from the peak to the valley of the load curve. The obtained results of the conventional TOU and ITOU models indicate that the proposed ITOU is more effective in achieving program goals.

Suggested Citation

  • Kholerdi, Somayeh Siahchehre & Ghasemi-Marzbali, Ali, 2021. "Interactive Time-of-use demand response for industrial electricity customers: A case study," Utilities Policy, Elsevier, vol. 70(C).
    • Handle: RePEc:eee:juipol:v:70:y:2021:i:c:s0957178721000266
    DOI: 10.1016/j.jup.2021.101192
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    References listed on IDEAS

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    1. Wang, Yong & Li, Lin, 2015. "Time-of-use electricity pricing for industrial customers: A survey of U.S. utilities," Applied Energy, Elsevier, vol. 149(C), pages 89-103.
    2. Aalami, H.A. & Moghaddam, M. Parsa & Yousefi, G.R., 2010. "Demand response modeling considering Interruptible/Curtailable loads and capacity market programs," Applied Energy, Elsevier, vol. 87(1), pages 243-250, January.
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    1. Ghasemi-Marzbali, Ali & Shafiei, Mohammad & Ahmadiahangar, Roya, 2023. "Day-ahead economical planning of multi-vector energy district considering demand response program," Applied Energy, Elsevier, vol. 332(C).
    2. Alirezaei, Roshan & Dashti, Reza & Mirhosseini, Mojtaba, 2025. "Analysis of energy supply scenarios using grid-connected hybrid systems: Investigating time-of-use demand response strategy for improving operational performance," Energy, Elsevier, vol. 318(C).
    3. Mossie, Alebachew T. & Khatiwada, Dilip & Palm, Bjorn & Bekele, Getachew, 2025. "Energy demand flexibility potential in cement industries: How does it contribute to energy supply security and environmental sustainability?," Applied Energy, Elsevier, vol. 377(PC).
    4. Roksana Yasmin & B. M. Ruhul Amin & Rakibuzzaman Shah & Andrew Barton, 2024. "A Survey of Commercial and Industrial Demand Response Flexibility with Energy Storage Systems and Renewable Energy," Sustainability, MDPI, vol. 16(2), pages 1-41, January.
    5. Hussain, Shahid & Teni, Abhishek Prasad & Hussain, Ihtisham & Hussain, Zakir & Pallonetto, Fabiano & Eichman, Josh & Irshad, Reyazur Rashid & Alwayle, Ibrahim M. & Alharby, Maher & Hussain, Md Asdaque, 2024. "Enhancing electric vehicle charging efficiency at the aggregator level: A deep-weighted ensemble model for wholesale electricity price forecasting," Energy, Elsevier, vol. 308(C).

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