Reliability evaluation of power-generating systems including time-dependent sources based on binary particle swarm optimization

Reliability evaluation of power generation systems using probabilistic methods has drawn much attention due to their capacity to account for system uncertainties. However, because of the large number of possible failure states involved in the power system, it is normally not viable to exhaustively enumerate and evaluate all the states which may contribute to system failure. Meanwhile, time-dependent sources such as wind turbine generators are being more significantly integrated into the traditional power grid for cleaner power generation. The intermittency of wind power sources further complicates the reliability evaluation process. In this paper, a binary particle swarm optimization (BPSO) is adopted to derive a set of meaningful system states, which significantly affects the adequacy indices of generation system including loss of load expectation (LOLE), loss of load frequency (LOLF), and expected energy not supplied (EENS). A numerical example is used to verify the applicability and validity of the proposed population-based intelligent search (PIS) based evaluation procedure. Especially, a comparative study in relation to the exact method and Monte Carlo simulation (MCS) is carried out.