Energy management is a key topic for today’s society, and a crucial challenge is to shift from a production system based on fossil fuel to sustainable energy. A key ingredients 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 and colleagues [12] where back up 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. Our results, shows that min-sum favorably compares with simulated annealing and it represents a promising solution method for this model.

Decentralized control for power distribution with ancillary lines in the smart grid

FARINELLI, Alessandro
2017-01-01

Abstract

Energy management is a key topic for today’s society, and a crucial challenge is to shift from a production system based on fossil fuel to sustainable energy. A key ingredients 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 and colleagues [12] where back up 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. Our results, shows that min-sum favorably compares with simulated annealing and it represents a promising solution method for this model.
2017
Decentralized constraint optimization, Factor graphs, Smart grid
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/963302
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