Abstract
The cooperation of agents in smart grids to form coalitions could bring benefit both for agent itself and the distribution power system. To tackle the problem as a game of partition form function poses significant computing challenges due to the huge search space for the optimization problem. In this paper, we propose a stochastic optimization approach using Population Based Incremental Learning (PBIL) algorithm with top-k Merit Weighting and a customized strategy for choosing the initial probability to solve the problem. Empirical results show that the proposed algorithm gives competitive performance compared with a few stochastic optimization algorithms.
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Notes
- 1.
Meteorological data obtained from “CliFlo: NIWA’s National Climate Database on the Web”.
- 2.
The code of the experiments is written and testing in Python 3.6 on Windows 7 PC with Intel core i5-4570 CPU and 16 GB RAM.
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Lee, S.HS., Deng, J.D., Peng, L., Purvis, M.K., Purvis, M. (2017). Top-k Merit Weighting PBIL for Optimal Coalition Structure Generation of Smart Grids. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10637. Springer, Cham. https://doi.org/10.1007/978-3-319-70093-9_18
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DOI: https://doi.org/10.1007/978-3-319-70093-9_18
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