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Solving electric power distribution uncertainty using deep learning and incentive-based demand response

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  • Palaniyappan, Balakumar
  • T, Vinopraba
  • Chandrasekaran, Geetha

Abstract

Recent Demand Response (DR) struggles with the end user's uncertainty in Electric Power Consumption (EPC), which affects the system's generation costs and stability. Incentive-based DR has offered to be an effective technique for mitigating supply and demand imbalances. However, it presents complex issues, such as electricity consumption uncertainty. This article proposes an incentive-based integrated DR model for Demand Side Management (DSM) program to handle the EPC uncertainty. In addition, the applicability of DR has been enhanced by the deep learning-based Bi-directional Long Short Term Memory (B-LSTM) model to forecast and curtail the load of the participated end users in the DSM program. Finally, results indicate that the proposed DSM program can achieve a win-win situation in reducing end-user uncertainty, lowering costs, and enhancing system stability.

Suggested Citation

  • Palaniyappan, Balakumar & T, Vinopraba & Chandrasekaran, Geetha, 2023. "Solving electric power distribution uncertainty using deep learning and incentive-based demand response," Utilities Policy, Elsevier, vol. 82(C).
    • Handle: RePEc:eee:juipol:v:82:y:2023:i:c:s0957178723000917
    DOI: 10.1016/j.jup.2023.101579
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    References listed on IDEAS

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    Cited by:

    1. 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).

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