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A PEM-based augmented IBDR framework and its evaluation in contemporary distribution systems

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  • Kansal, Gaurav
  • Tiwari, Rajive

Abstract

Demand response (DR) is an attractive concept that invites customers’ active participation in the distribution sector by means of price elasticity of demand (PED). It not only enhances customers’ demand sensitivity but also improves technicalities and economics related to both the utility and demand sides. This paper emphasizes the combined effect of price-based DR (PBDR) and incentive-based DR (IBDR) with the inclusion of PED. The elasticity phenomenon, when applied with incentives as in IBDR, changes the demand-consumption pattern as compared to individual DR. Moreover, the demand variation due to only incentives leads to incentive elasticity, which needs to be studied carefully; then only the impact of individual DR and augmented DR (PBDR and IBDR combined) can be understood analytically. In this work, IBDR models are tested on considered pricing schemes along with a new proposed pricing scheme to evaluate the systems’ technical and economical parameters. A standard IEEE 33 bus distribution system has been chosen for the assessment of suggested models and to compare them to the existing ones. Furthermore, these models are descriptively evaluated from both the utility and consumer perspectives.

Suggested Citation

  • Kansal, Gaurav & Tiwari, Rajive, 2024. "A PEM-based augmented IBDR framework and its evaluation in contemporary distribution systems," Energy, Elsevier, vol. 296(C).
  • Handle: RePEc:eee:energy:v:296:y:2024:i:c:s0360544224008740
    DOI: 10.1016/j.energy.2024.131102
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    References listed on IDEAS

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    1. Dagoumas, Athanasios S. & Polemis, Michael L., 2017. "An integrated model for assessing electricity retailer’s profitability with demand response," Applied Energy, Elsevier, vol. 198(C), pages 49-64.
    2. Wen, Lulu & Zhou, Kaile & Li, Jun & Wang, Shanyong, 2020. "Modified deep learning and reinforcement learning for an incentive-based demand response model," Energy, Elsevier, vol. 205(C).
    3. Xu, Bo & Wang, Jiexin & Guo, Mengyuan & Lu, Jiayu & Li, Gehui & Han, Liang, 2021. "A hybrid demand response mechanism based on real-time incentive and real-time pricing," Energy, Elsevier, vol. 231(C).
    4. Morales-España, Germán & Martínez-Gordón, Rafael & Sijm, Jos, 2022. "Classifying and modelling demand response in power systems," Energy, Elsevier, vol. 242(C).
    5. Shahryari, E. & Shayeghi, H. & Mohammadi-ivatloo, B. & Moradzadeh, M., 2018. "An improved incentive-based demand response program in day-ahead and intra-day electricity markets," Energy, Elsevier, vol. 155(C), pages 205-214.
    6. Heydarian-Forushani, Ehsan & Golshan, Mohamad Esmail Hamedani & Shafie-khah, Miadreza & Catalão, João P.S., 2020. "A comprehensive linear model for demand response optimization problem," Energy, Elsevier, vol. 209(C).
    7. Moghaddam, M. Parsa & Abdollahi, A. & Rashidinejad, M., 2011. "Flexible demand response programs modeling in competitive electricity markets," Applied Energy, Elsevier, vol. 88(9), pages 3257-3269.
    8. Astriani, Yuli & Shafiullah, GM & Shahnia, Farhad, 2021. "Incentive determination of a demand response program for microgrids," Applied Energy, Elsevier, vol. 292(C).
    9. Imani, Mahmood Hosseini & Ghadi, M. Jabbari & Ghavidel, Sahand & Li, Li, 2018. "Demand Response Modeling in Microgrid Operation: a Review and Application for Incentive-Based and Time-Based Programs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 486-499.
    10. Luft, Joan, 1994. "Bonus and penalty incentives contract choice by employees," Journal of Accounting and Economics, Elsevier, vol. 18(2), pages 181-206, September.
    11. Dewangan, Chaman Lal & Vijayan, Vineeth & Shukla, Devesh & Chakrabarti, S. & Singh, S.N. & Sharma, Ankush & Hossain, Md. Alamgir, 2023. "An improved decentralized scheme for incentive-based demand response from residential customers," Energy, Elsevier, vol. 284(C).
    12. Lu, Renzhi & Hong, Seung Ho, 2019. "Incentive-based demand response for smart grid with reinforcement learning and deep neural network," Applied Energy, Elsevier, vol. 236(C), pages 937-949.
    13. Yousefi, Shaghayegh & Moghaddam, Mohsen Parsa & Majd, Vahid Johari, 2011. "Optimal real time pricing in an agent-based retail market using a comprehensive demand response model," Energy, Elsevier, vol. 36(9), pages 5716-5727.
    14. Mulder, Machiel & Willems, Bert, 2019. "The Dutch retail electricity market," Energy Policy, Elsevier, vol. 127(C), pages 228-239.
    15. Suchitra Dayalan & Sheikh Suhaib Gul & Rajarajeswari Rathinam & George Fernandez Savari & Shady H. E. Abdel Aleem & Mohamed A. Mohamed & Ziad M. Ali, 2022. "Multi-Stage Incentive-Based Demand Response Using a Novel Stackelberg–Particle Swarm Optimization," Sustainability, MDPI, vol. 14(17), pages 1-25, September.
    16. Verma, Mandhir Kumar & Mukherjee, V. & Kumar Yadav, Vinod & Ghosh, Santosh, 2020. "Indian power distribution sector reforms: A critical review," Energy Policy, Elsevier, vol. 144(C).
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