Smart Grid Inspired Future Technologies. Second EAI International Conference, SmartGIFT 2017, London, UK, March 27–28, 2017, Proceedings

Research Article

Scheduling Domestic Shiftable Loads in Smart Grids: A Learning Automata-Based Scheme

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  • @INPROCEEDINGS{10.1007/978-3-319-61813-5_6,
        author={Rajan Thapa and Lei Jiao and B. Oommen and Anis Yazidi},
        title={Scheduling Domestic Shiftable Loads in Smart Grids: A Learning Automata-Based Scheme},
        proceedings={Smart Grid Inspired Future Technologies. Second EAI International Conference, SmartGIFT 2017, London, UK, March 27--28, 2017, Proceedings},
        proceedings_a={SMARTGIFT},
        year={2017},
        month={9},
        keywords={Smart Grid Load scheduling Potential Game Nash Equilibrium Learning Automata},
        doi={10.1007/978-3-319-61813-5_6}
    }
    
  • Rajan Thapa
    Lei Jiao
    B. Oommen
    Anis Yazidi
    Year: 2017
    Scheduling Domestic Shiftable Loads in Smart Grids: A Learning Automata-Based Scheme
    SMARTGIFT
    Springer
    DOI: 10.1007/978-3-319-61813-5_6
Rajan Thapa1, Lei Jiao1,*, B. Oommen2, Anis Yazidi3
  • 1: University of Agder
  • 2: Carleton University
  • 3: Oslo and Akershus University College of Applied Sciences
*Contact email: lei.jiao@uia.no

Abstract

In this paper, we consider the problem of scheduling shiftable loads, over multiple users, in smart grids. We approach the problem, which is becoming increasingly pertinent in our present energy-thirsty society, using a novel game-theoretic framework. From a modeling perspective, the scheduling problem is formulated as a game, and in particular, a so-called “Potential” game. This game has at least one pure strategy Nash Equilibrium (NE), and we demonstrate that the NE point is a global optimal point. The solution that we propose, which is the pioneering solution that incorporates the theory of Learning Automata (LA), permits the total supplied loads to approach the power budget of the subnet once the algorithm has converged to the NE point. The scheduling is achieved by attaching a LA to each customer. The paper discusses the applicability of three different LA schemes, and in particular the recently-introduced Bayesian Learning Automata (BLA). Numerical results (The algorithmic details and the experimental results presented here are limited in the interest of space. More detailed explanations of these are found in [13]), obtained from testing the schemes on numerous simulated datasets, demonstrate the speed and the accuracy of proposed algorithms in terms of their convergence to the game’s NE point.