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

Nicolo Gionfra (1) , Guillaume Sandou (1, 2) , Houria Siguerdidjane (1) , Philippe Loevenbruck (3) , Damien Faille (4)

1 L2S - Laboratoire des signaux et systèmes
2 DISCO - Dynamical Interconnected Systems in COmplex Environments
3 EDF - EDF
4 EDF R&D STEP - Simulation et Traitement de l'information pour l'Exploitation des systèmes de Production

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.

Domaines: Automatique / Robotique

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, ⟨10.1109/ccta.2017.8062537⟩. ⟨hal-01667868⟩