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 C O 2 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.
Decentralized Power Distribution in the Smart Grid with Ancillary Lines
Bistaffa, Filippo;Farinelli, Alessandro
2019-01-01
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 C O 2 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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