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Quadratic Function based Price Adjustment Strategy on Monitoring Process of Power Consumption Load in Smart Grid

Lookup NU author(s): Professor Jingxin DongORCiD

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).


Abstract

© 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.


Publication metadata

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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Funding

Funder referenceFunder name
2019KJFZ048
2020KJFZ034
1P16303003
2018KJFZ035
71572113
71871144
National Natural Science Foundation of China
NSFC
XJ2021150
XJ2021160
XJ2021165
XJ2021191
XJ2021206

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