| dc.description.abstract | In this paper, we consider the problem of scheduling shiftable loads, over multiple users,
in smart electrical grids. We approach the problem, which is becoming increasingly pertinent in our present
energy-thirsty society, using a novel
distributed
game-theoretic framework. In our specific instantiation,
we consider the scenario when the power system has a local-area Smart Grid subnet comprising of a single
power source and multiple customers. The objective of the exercise is to tacitly control the total power
consumption of the customers’ shiftable loads, so to approach the rigid power budget determined by the
power source, but to simultaneously not exceed this threshold. As opposed to the ‘‘traditional’’ paradigm that
utilizes a central controller to achieve the load scheduling, we seek to achieve this by pursuing a distributed
approach that allows the users
1
to make individual decisions by invoking negotiations with other customers.
The decisions are essentially of the sort, where the individual users can choose whether they want to be
supplied or not. 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
utilizes 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. Numerical results, obtained from testing the schemes
on numerous simulated data sets, demonstrate the speed and the accuracy of proposed algorithms in terms
of their convergence to the game’s NE point. | en |
