@incollection{eprints3580,
          series = {Lecture Notes in Computer Science},
           pages = {54--69},
           title = {Power Trading Coordination in Smart Grids Using Dynamic Learning and Coalitional Game Theory},
            note = {SCOPUS ID: 2-s2.0-84944753708; WOS Accession Number: WOS:000363574600006},
          number = {9259},
            year = {2015},
       publisher = {Springer},
       booktitle = {Quantitative Evaluation of Systems.12th International Conference, QEST 2015, Madrid, Spain, September 1-3, 2015, Proceedings},
          author = {Farshad Shams and Mirco Tribastone},
             url = {http://eprints.imtlucca.it/3580/},
        abstract = {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.}
}