Assessment of Optimum Operating State of HVDC Converters Under Uncertain Operating Condition of Hybrid AC-DC Power System Through a Stochastic Optimization Based Approach

In previously reported studies on optimal power flow (OPF) in hybrid AC-DC systems, the assessment of the operating state and its effect on converter operation and performance is mostly ignored. Further, the uncertainty involved with renewable energy sources (RES) affects the state variables (SV) of the direct current (DC) link side, and the study on its implication on converter performance is unexplored. Hence, in this study, converters' operating state and performance under uncertain RES power and load demand are assessed through a stochastic optimal power flow (SOPF) based approach. The expected generation cost, including overestimation and underestimation penalty, is considered as an objective function. The simulation study is carried out on a modified IEEE 30 bus system, and the honey badger algorithm (HBA) is utilized as an optimization tool. The probability distribution (PD) of firing angle, power factor (PF), ripple factor (RF) etc. are evaluated through Monte-Carlo simulation, and assessment of them in terms of mean, range, and spread is presented. For instance, at the rectifier end, the values of PF and RF range between 0.974 to 0.984 and 0.068 to 0.082, with 0.979 and 0.075 as mean values respectively. A comparison with other reported results indicates the robustness of the proposed method in achieving better converter performance under uncertain conditions.