Optimal power flow based siting of renewable generators and power electronic devices under parameter uncertainty

With rising clean energy penetration in distribution networks, optimizing the siting and capacity determination of distributed wind-solar generators and power electronic equipment is crucial for improving power flow and enhancing power quality. The article proposes a joint siting optimization framework, which builds a model based on flexible distribution network optimal power flow, takes minimum system generation cost as the objective function and incorporates polynomial chaos expansion to address wind-solar output uncertainties. Adopting a bi-level optimization structure, the lower level relaxes the optimal power flow model into a convex problem via semidefinite relaxation and reconstructs the convex model by adopting the Lagrangian dual method, while the upper level uses an improved snow geese algorithm for iterative siting optimization. Validated on IEEE 15/33/69/141-bus systems and compared with five peer algorithms, experimental results show that, under identical conditions, compared with deploying devices at fixed nodes, the optimal installation nodes determined by the algorithm reduce the unit operating cost by 1.12%, 2.06%, 4.54% and 1.32%, respectively. Besides, the improved algorithm ranks first in Friedman tests across all cases.