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Decentralized Power Distribution in the Smart Grid with Ancillary Lines

An Approach Based on Distributed Constraint Optimization

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Abstract

Energy management is a key topic for today’s society, and a crucial challenge is the shift from a production system based on fossil fuel to sustainable energy. A key ingredient for this important step is the use of a highly automated power delivery network, where intelligent devices can communicate and collaborate to optimize energy management. This paper investigates a specific model for smart power grids initially proposed by Zdeborov et al. (Phys Rev E Stat Nonlinear Soft Matter Phys 80(4): 2009) where backup power lines connect a subset of loads to generators so to meet the demand of the whole network. Specifically, we extend such model to minimize CO2 emissions related to energy production. In more detail, we propose a formalization for this problem based on the Distributed Constraint Optimization Problem (DCOP) framework and a solution approach based on the min-sum algorithm. We empirically evaluate our approach on a set of benchmarking power grid instances comparing our proposed solution to simulated annealing and to the DSA algorithm. Our results show that min-sum favorably compares with simulated annealing and DSA providing a promising solution method for this model.

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Notes

  1. Note that, an initial version of this paper appeared in [13]. With respect to that contribution, here we provide more results on the performance of min-sum, and, specifically, we compare min-sum with DSA (see Section 4.3).

  2. The code is available from https://github.com/mr2c12/jmaxsum.

  3. In all the plots, the error bars represent the confidence interval of a t-test with 95% accuracy.

  4. This can be easily observed when M = 10000 and M = 20000: when \(\bar {x}=0.3\) the instances with M = 20000 require approximately double the time required by the instances with M = 10000.

  5. Results are generated by comparing the results of the two algorithms on the same instance and computing the percentage over all instances.

  6. We refer the reader to [15] for a more detailed description of the different variants of DSA.

  7. This is the maximum number of steps we use for min-sum.

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Authors and Affiliations

  1. Computer Science Department, University of Verona, Verona, Italy

    Michele Roncalli, Filippo Bistaffa & Alessandro Farinelli

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  1. Michele Roncalli
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  2. Filippo Bistaffa
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  3. Alessandro Farinelli
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Correspondence to Alessandro Farinelli.

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Roncalli, M., Bistaffa, F. & Farinelli, A. Decentralized Power Distribution in the Smart Grid with Ancillary Lines. Mobile Netw Appl 24, 1654–1662 (2019). https://doi.org/10.1007/s11036-017-0893-y

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  • DOI: https://doi.org/10.1007/s11036-017-0893-y

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