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 distributed game-theoretic framework. From a modeling perspective, the distributed 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.
Chancellor’s Professor; Fellow: IEEE and Fellow: IAPR. This author is also an Adjunct Professor with the University of Agder, Norway. The work of this author was partially supported by NSERC, the Natural Sciences and Engineering Research Council of Canada.
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Notes
- 1.
If \(\exists i\), \(s.t.~L_i > C_{SL}\), our solution is applicable by excluding the users whose demands exceed the capacity. Thus, we will not elaborate on this scenario in any greater detail.
- 2.
Additional results can be seen from the technical report of the First Author, and can be made available on request.
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© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Thapa, R., Jiao, L., Oommen, B.J., Yazidi, A. (2017). Scheduling Domestic Shiftable Loads in Smart Grids: A Learning Automata-Based Scheme. In: Lau, E., et al. Smart Grid Inspired Future Technologies. SmartGift 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 203. Springer, Cham. https://doi.org/10.1007/978-3-319-61813-5_6
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