relation: http://eprints.imtlucca.it/3580/
title: Power Trading Coordination in Smart Grids Using Dynamic Learning and Coalitional Game Theory
creator: Shams, Farshad
creator: Tribastone, Mirco
subject: QA76 Computer software
description: In traditional power distribution models, consumers acquire power from the central distribution unit, while “micro-grids” in a smart power grid can also trade power between themselves. In this paper, we investigate the problem of power trading coordination among such micro-grids. Each micro-grid has a surplus or a deficit quantity of power to transfer or to acquire, respectively. A coalitional game theory based algorithm is devised to form a set of coalitions. The coordination among micro-grids determines the amount of power to transfer over each transmission line in order to serve all micro-grids in demand by the supplier micro-grids and the central distribution unit with the purpose of minimizing the amount of dissipated power during generation and transfer. We propose two dynamic learning processes: one to form a coalition structure and one to provide the formed coalitions with the highest power saving. Numerical results show that dissipated power in the proposed cooperative smart grid is only 10% of that in traditional power distribution networks.
publisher: Springer
date: 2015
type: Book Section
type: PeerReviewed
identifier:   Shams, Farshad and Tribastone, Mirco  Power Trading Coordination in Smart Grids Using Dynamic Learning and Coalitional Game Theory.   In:  Quantitative Evaluation of Systems.12th International Conference, QEST 2015, Madrid, Spain, September 1-3, 2015, Proceedings.   Lecture Notes in Computer Science  (9259).  Springer, pp. 54-69.  ISBN 978-3-319-22264-6      (2015)  
relation: http://doi.org/10.1007/978-3-319-22264-6_4
relation: doi:10.1007/978-3-319-22264-6_4