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.
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Notes
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This circuit is suitable for analysing its symmetrical three-phase operation.
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This work is supported by the EU project QUANTICOL, 600708.
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Shams, F., Tribastone, M. (2015). Power Trading Coordination in Smart Grids Using Dynamic Learning and Coalitional Game Theory. In: Campos, J., Haverkort, B. (eds) Quantitative Evaluation of Systems. QEST 2015. Lecture Notes in Computer Science(), vol 9259. Springer, Cham. https://doi.org/10.1007/978-3-319-22264-6_4
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