TY  - CHAP
ID  - eprints3580
EP  - 69
UR  - http://doi.org/10.1007/978-3-319-22264-6_4
PB  - Springer
SP  - 54
AV  - none
SN  - 0302-9743
T2  - Quantitative Evaluation of Systems.12th International Conference, QEST 2015, Madrid, Spain, September 1-3, 2015, Proceedings
Y1  - 2015///
T3  - Lecture Notes in Computer Science
A1  - Shams, Farshad
A1  - Tribastone, Mirco
N2  - 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.
N1  - SCOPUS ID: 2-s2.0-84944753708; WOS Accession Number: WOS:000363574600006
TI  - Power Trading Coordination in Smart Grids Using Dynamic Learning and Coalitional Game Theory
ER  -