Neural Computing and Applications

, Volume 32, Issue 1, pp 139–149 | Cite as

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

  • Hang Qin
  • Zhongbo WuEmail author
  • Min Wang
Brain- Inspired computing and Machine learning for Brain Health


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.


Smart grid Distributed demand-side management Scheduling scheme Stochastic linear programming game 



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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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.Computer SchoolYangtze UniversityJingzhouChina
  2. 2.School of Computer EngineeringHubei University of Arts and ScienceXiangyangChina

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