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Demand-side management for smart grid networks using stochastic linear programming game

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Abstract

This paper analyzes the mode provisioning and scheduling, in light of the aggregation over distributed energy storage system for improving the interactions and energy trading decisions under the smart grid networks. Further a new smart power system equipped with energy storage devices yields efficiency and robustness in a novel structure, which can identify and react on the energy market equilibrium in a timely manner. An energy consumption and stochastic linear programming game in the distributed structure is proposed for the energy payments, so that scheduling for appliances and storage devices can be used here as well. Furthermore, it is easy to implement a proposed two-phase DSLPM (distributed stochastic linear programming management) algorithm to bring about optimality with both energy provider and users to approach payoff sharing under uncertainty. With the incomplete information, a price equilibrium scheme is proposed. Experimental results are shown to verify the consumed energy, payment, and convergence properties of the proposed models.

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Acknowledgements

The authors acknowledge the National Nature Science Foundation of China (Nos. 61440023, 61202046), China National Petroleum Corporation Creative Research Foundation (No. 2013D-5006-0605) and Discipline Groups Construction Foundation of Food New-type Industrialization of Hubei University of Arts and Science.

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Authors and Affiliations

  1. Computer School, Yangtze University, Jingzhou, 434023, China

    Hang Qin

  2. School of Computer Engineering, Hubei University of Arts and Science, Xiangyang, 441053, China

    Zhongbo Wu & Min Wang

Authors
  1. Hang Qin
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  2. Zhongbo Wu
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  3. Min Wang
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Correspondence to Zhongbo Wu.

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Qin, H., Wu, Z. & Wang, M. Demand-side management for smart grid networks using stochastic linear programming game. Neural Comput & Applic 32, 139–149 (2020). https://doi.org/10.1007/s00521-018-3787-4

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  • DOI: https://doi.org/10.1007/s00521-018-3787-4

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