Probabilistic Optimal Power Flow of an AC/DC System with a Multiport Current Flow Controller

To evaluate the impact of the randomness and correlation of photovoltaic (PV) and load on AC/DC systems with a multiport current flow controller (M-CFC), this paper proposes a probabilistic optimal power flow calculation for AC/DC systems, based on a nonparametric kernel density estimation. First, according to the M-CFC model, the DC power flow calculation method with M-CFC was inferred, and its influence on line loss was analyzed. Second, a nonparametric kernel density estimation with an adaptive bandwidth is used to accurately describe the probability distribution of the PV and load, and correlation samples of the PV and load are obtained by the mixed copula function. Then an optimization model that considers system loss and static security is established, and a fast nondominated sorting genetic algorithm based on the elite strategy (NSGA-II) is used to calculate the multi-objective probability optimal power flow of the AC/DC system. Finally, a case study is performed on a modified IEEE39 bus system using measured PV and load data. We verified that the nonparametric kernel density estimation with an adaptive bandwidth can better adapt to random component uncertainty, and M-CFC can improve the static security of the system.