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Privacy masking distributed saddle-point algorithm for dynamic economic dispatch

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Abstract

In smart grids, the goal of the dynamic economic dispatch problem (DEDP) is to obtain the optimal dispatch schedule for each generating unit in a set of periods under certain constraints. A major challenge is that privacy disclosures possibly occur during the exchange and updating of communications. To address the issue, we propose a fully distributed saddle-point algorithm while preserving the privacy of participants by injecting the decaying Laplace noise. Based on the properties of the multi-Lyapunov function, we prove that the algorithm has an asymptotic convergence in the sense of expectation. Using the mechanism of differential privacy, we prove that the algorithm can guarantee \(\varepsilon\)-differential privacy. In addition, we characterize the trade-off between levels of differential privacy and algorithmic accuracy. Finally, numerical simulations on IEEE 30-bus and IEEE 118-bus are used to validate the theoretical results.

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Availability of data and materials

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China under grants No. 62073344, No. 11971083 and the Natural Science Foundation Projection of Chongqing CSTC under grant No. cstc2020jcyj-msxmX0287.

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Authors and Affiliations

  1. Central South University, Changsha, 410000, China

    Kaihui Xu & Guo Chen

  2. Chongqing Normal University, Chongqing, 401331, China

    Jueyou Li

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  1. Kaihui Xu
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Correspondence to Guo Chen.

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Cite this article

Xu, K., Li, J. & Chen, G. Privacy masking distributed saddle-point algorithm for dynamic economic dispatch. Neural Comput & Applic 35, 8109–8123 (2023). https://doi.org/10.1007/s00521-022-08089-1

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