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

Balakumar Palaniyappan, Vinopraba T and Geetha Chandrasekaran

Utilities Policy, 2023, vol. 82, issue C

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.

Keywords: Deep learning; Demand response program; Uncertainty analysis; Short term forecasting; Smart grid; Internet of things (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:juipol:v:82:y:2023:i:c:s0957178723000917

DOI: 10.1016/j.jup.2023.101579

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