Power system optimization under uncertainties: A PSO approach


Most power systems optimization problems have to be solved under uncertainty. The scenarios used for modeling the uncertainties should be able to represent their stochastic nature. If this requires huge sampling, particle swarm optimization (PSO) based scenario reduction technique can be a good option to approximate the initial scenario distribution. This paper proposes a multi-stage model for the optimal operation of a wind integrated power system. A parameter free self learning particle swarm optimization algorithm has been used to solve the deterministic and stochastic models. The robustness of the solution procedure has been verified by the effective utilization of the various generation units.

DOI: 10.1109/SIS.2008.4668276

Cite this paper

@article{Pappala2008PowerSO, title={Power system optimization under uncertainties: A PSO approach}, author={Venkata Swaroop Pappala and Istv{\'a}n Erlich}, journal={2008 IEEE Swarm Intelligence Symposium}, year={2008}, pages={1-8} }