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An improved decentralized scheme for incentive-based demand response from residential customers

Author

Listed:
  • Dewangan, Chaman Lal
  • Vijayan, Vineeth
  • Shukla, Devesh
  • Chakrabarti, S.
  • Singh, S.N.
  • Sharma, Ankush
  • Hossain, Md. Alamgir

Abstract

Demand response is becoming increasingly important due to the high penetration of intermittent and variable renewable energy and electric vehicles in power systems. Real-time Incentive-Based Demand Response (IBDR) is helpful for power balancing in normal and emergency scenarios. The paper focuses on making an efficient IBDR scheme and increasing the participation of residential customers. A feature in the Home Energy Management System (HEMS) that provides the quantity of flexible load demand available with the residential customers for real-time IBDR in a decentralized scheme is explored. This paper analyzes the scheduling of flexible residential appliances in a dispersed manner over time by HEMS to increase the participation of residential customers in real-time IBDR. The error accumulation in the IBDR in a decentralized scheme due to the discrete character of residential appliances is examined and addressed. A financial ratio is proposed to give a fair opportunity to all the participants in IBDR when the cumulative flexible load demand available with the participants is greater than the required quantity of demand response. This paper provides algorithms that improve demand response systems by increasing the flexibility available to participants, reducing the error accumulation in IBDR, and ensuring the fairness of the IBDR opportunity to participants. The simulation analysis is done with 400 residential customers with a different number of flexible appliances and different energy requirements. The paper highlights the potential benefits of using a decentralized scheme to enable real-time IBDR in power systems.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:284:y:2023:i:c:s036054422301962x
    DOI: 10.1016/j.energy.2023.128568
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    References listed on IDEAS

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    Cited by:

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    2. Rabie, Dalia & Farzaneh, Hooman, 2026. "A novel modeling framework for demand response-based energy management systems in smart electricity markets, using optimization and multi-criteria decision making techniques," Applied Energy, Elsevier, vol. 405(C).
    3. Zhai, Chao & Cao, Zhixiang & Wang, Yi & Abdou-Tankari, Mahamadou & Yu, Jian & Lei, Zhen, 2025. "A reverse incentive-based demand response strategy for shared energy storage in industrial microgrids: Optimization, scheduling, and investment analysis," Energy, Elsevier, vol. 330(C).
    4. Hussain, Jawad & Huang, Qi & Li, Jian & Hussain, Fazal & Mirjat, Baqir Ali & Zhang, Zhenyuan & Ahmed, Syed Adrees, 2024. "A fully decentralized demand response and prosumer peer-to-peer trading for secure and efficient energy management of community microgrid," Energy, Elsevier, vol. 312(C).
    5. Tan, Xinquan & Tan, Mao & Li, Zibin & Wang, Rui & Peng, Hanmei & Zou, Juan, 2025. "Preference-based multi-objective energy retail package for efficient and flexible demand response of energy community," Energy, Elsevier, vol. 338(C).
    6. Kansal, Gaurav & Tiwari, Rajive, 2024. "A PEM-based augmented IBDR framework and its evaluation in contemporary distribution systems," Energy, Elsevier, vol. 296(C).
    7. P, Balakumar & Ramu, Senthil Kumar & T, Vinopraba, 2024. "Optimizing electric vehicle charging in distribution networks: A dynamic pricing approach using internet of things and Bi-directional LSTM model," Energy, Elsevier, vol. 294(C).
    8. Vibhute, Siddhant & Kowli, Anupama & Kulkarni, Ankur A., 2025. "Improved load approximation models and tractable solution schemes for home energy management," Energy, Elsevier, vol. 335(C).

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