Reliability Assessment of Power System Based on the Dichotomy Method

With a sustainable increase in the scale of power system, the number of states in the state space grows exponentially, and the reliability assessment of the power system faces enormous challenges. Traditional assessment methods, such as state enumeration (SE) and Monte Carlo simulation (MCS) methods, have encountered performance bottlenecks in terms of efficiency and accuracy. In this paper, the lattice theory of the state space was studied, and a dichotomy method was proposed to efficiently partition the state space into some disjoint sub-lattices with a small number of optimal power flow (OPF) operations. Based on dichotomy method, the reliability indices of the entire space can be calculated lattice-by-lattice. Alone with the partitioning procedure, the calculated loss of load probability (LOLP) monotonically increases and rapidly tends to the analytic value. Moreover, we designed a customized Monte Carlo sampling method in lattices of interest to compute expected energy not supply (EENS). The experiments are conducted on the RBTS and RTS-79 systems. The results show that the proposed method achieves the analytic LOLP of the RBTS system after 578 of OPF operations, which is 1,673 of times faster than traditional SE methods, and the designed Monte Carlo sampling method converged after 6,578 of OPF operations on test systems which is 268 of times faster than MCS methods. In the RTS system, the proposed method achieves higher computational efficiency while improving accuracy, with speed improvements of 153 and 14 times compared to SE and MCS, respectively.