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Lookup NU author(s): Professor Jingxin DongORCiD
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
© 2021 Elsevier Ltd. Based on the data collected from smart meters, electricity pricing models can be developed to balance power supply and demand in time slot and obtain the optimal consumption loads and prices. However, in real life, users’ reserved consumption requirement loads sometimes deviate significantly from the optimal consumption loads obtained from models, which results in overloaded power systems or even power cuts. To address this issue, an engineering process control strategy has been proposed in this paper to minimize the difference between the optimal and the users’ reserved consumption requirement loads. We proposed an exponential weighted moving average model to predict the load difference in future time slots, and also developed a novel quadratic function based demand response mechanism to adjust the power price for power providers. The demand response mechanism can be used to adjust the price in the future time slots when the predicted demand exceeds the upper or lower boundary. Simulation results indicate that the quadratic function adjustment strategy has excellent performance in a practical power market in Singapore. Compared with the linear function based adjustment method, the proposed quadratic function based adjustment method decreases the adjustment times and standard errors of residuals, and increases the social welfare and power suppliers’ profits under the same boundary conditions. In addition, the performance of the proposed strategy demonstrated its competency in peak-cutting and valley-filling and balancing energy provision with demands.
Author(s): He BJ, Li JX, Li D, Dong JX, Zhu L
Publication type: Article
Publication status: Published
Journal: International Journal of Electrical Power and Energy Systems
Year: 2022
Volume: 134
Print publication date: 01/01/2022
Online publication date: 16/07/2021
Acceptance date: 22/04/2021
Date deposited: 22/04/2021
ISSN (print): 0142-0615
ISSN (electronic): 1879-3517
Publisher: Elsevier Ltd
URL: https://doi.org/10.1016/j.ijepes.2021.107124
DOI: 10.1016/j.ijepes.2021.107124
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