A Novel Distributed Particle Swarm Optimization Algorithm for the Optimal Power Flow Problem

Abstract : The distributed optimal power flow problem is addressed. No assumptions on the problem cost function, and network topology are needed to solve the optimization problem. A distributed particle swarm optimization algorithm is proposed, based on Deb's rule to handle hard constraints. Moreover, the approach enables to treat a class of distributed optimization problems in which the agents share a common optimization variable. Under mild communication assumptions, agents are only required to know local variables, cost function, and constraints to solve a common optimization problem. A simulation example is provided, based on a 5-bus electric grid.
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Communication dans un congrès
1st IEEE Conference on Control Technology and Applications (CCTA 2017), Aug 2017, Kohala Coast, United States. pp.1-8, 2017, Proceedings of the 1st IEEE Conference on Control Technology and Applications. 〈10.1109/ccta.2017.8062537 〉
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Soumis le : mardi 19 décembre 2017 - 16:05:14
Dernière modification le : jeudi 26 avril 2018 - 17:24:33

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Nicolo Gionfra, Guillaume Sandou, Houria Siguerdidjane, Philippe Loevenbruck, Damien Faille. A Novel Distributed Particle Swarm Optimization Algorithm for the Optimal Power Flow Problem. 1st IEEE Conference on Control Technology and Applications (CCTA 2017), Aug 2017, Kohala Coast, United States. pp.1-8, 2017, Proceedings of the 1st IEEE Conference on Control Technology and Applications. 〈10.1109/ccta.2017.8062537 〉. 〈hal-01667868〉

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