60,998 research outputs found
A Learning Automaton-based Scheme for Scheduling Domestic Shiftable Loads in Smart Grids
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 (SG) 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 users¹ 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 utilize
A Learning Automaton-based Scheme for Scheduling Domestic Shiftable Loads in Smart Grids
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 (SG) 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 users¹ 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 (BLA). Numerical results, 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.publishedVersionNivå
A Distributed Demand-Side Management Framework for the Smart Grid
This paper proposes a fully distributed Demand-Side Management system for
Smart Grid infrastructures, especially tailored to reduce the peak demand of
residential users. In particular, we use a dynamic pricing strategy, where
energy tariffs are function of the overall power demand of customers. We
consider two practical cases: (1) a fully distributed approach, where each
appliance decides autonomously its own scheduling, and (2) a hybrid approach,
where each user must schedule all his appliances. We analyze numerically these
two approaches, showing that they are characterized practically by the same
performance level in all the considered grid scenarios. We model the proposed
system using a non-cooperative game theoretical approach, and demonstrate that
our game is a generalized ordinal potential one under general conditions.
Furthermore, we propose a simple yet effective best response strategy that is
proved to converge in a few steps to a pure Nash Equilibrium, thus
demonstrating the robustness of the power scheduling plan obtained without any
central coordination of the operator or the customers. Numerical results,
obtained using real load profiles and appliance models, show that the
system-wide peak absorption achieved in a completely distributed fashion can be
reduced up to 55%, thus decreasing the capital expenditure (CAPEX) necessary to
meet the growing energy demand
Distributed power allocation for D2D communications underlaying/overlaying OFDMA cellular networks
The implementation of device-to-device (D2D) underlaying or overlaying
pre-existing cellular networks has received much attention due to the potential
of enhancing the total cell throughput, reducing power consumption and
increasing the instantaneous data rate. In this paper we propose a distributed
power allocation scheme for D2D OFDMA communications and, in particular, we
consider the two operating modes amenable to a distributed implementation:
dedicated and reuse modes. The proposed schemes address the problem of
maximizing the users' sum rate subject to power constraints, which is known to
be nonconvex and, as such, extremely difficult to be solved exactly. We propose
here a fresh approach to this well-known problem, capitalizing on the fact that
the power allocation problem can be modeled as a potential game. Exploiting the
potential games property of converging under better response dynamics, we
propose two fully distributed iterative algorithms, one for each operation mode
considered, where each user updates sequentially and autonomously its power
allocation. Numerical results, computed for several different user scenarios,
show that the proposed methods, which converge to one of the local maxima of
the objective function, exhibit performance close to the maximum achievable
optimum and outperform other schemes presented in the literature
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